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<h1>Source code for pyFTS.models.multivariate.mvfts</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span>
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span><span class="p">,</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">mvflrg</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
<span class="kn">from</span> <span class="nn">types</span> <span class="k">import</span> <span class="n">LambdaType</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<div class="viewcode-block" id="product_dict"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.product_dict">[docs]</a><span class="k">def</span> <span class="nf">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Code by Seth Johnson</span>
<span class="sd"> :param kwargs:</span>
<span class="sd"> :return:</span>
<span class="sd"> """</span>
<span class="n">keys</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
<span class="n">vals</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">vals</span><span class="p">):</span>
<span class="k">yield</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">instance</span><span class="p">))</span></div>
<div class="viewcode-block" id="MVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS">[docs]</a><span class="k">class</span> <span class="nc">MVFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Multivariate extension of Chen's ConventionalFTS method</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'explanatory_variables'</span><span class="p">,[])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'target_variable'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"MVFTS"</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Multivariate FTS"</span>
<div class="viewcode-block" id="MVFTS.append_variable"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_variable">[docs]</a> <span class="k">def</span> <span class="nf">append_variable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">var</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Append a new endogenous variable to the model</span>
<span class="sd"> :param var: variable object</span>
<span class="sd"> :return:</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="p">)</span></div>
<div class="viewcode-block" id="MVFTS.format_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">[docs]</a> <span class="k">def</span> <span class="nf">format_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
<span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">extractor</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">])</span>
<span class="k">return</span> <span class="n">ndata</span></div>
<div class="viewcode-block" id="MVFTS.apply_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.apply_transformations">[docs]</a> <span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">updateUoD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1">#if isinstance(ndata, pd.DataFrame) else ndata[var.data_label]</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="ow">and</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'common'</span><span class="p">:</span>
<span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">values</span><span class="p">,</span>
<span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
<span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">return</span> <span class="n">ndata</span></div>
<div class="viewcode-block" id="MVFTS.generate_lhs_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs">[docs]</a> <span class="k">def</span> <span class="nf">generate_lhs_flrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">vc</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
<span class="n">data_point</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
<span class="n">lags</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">tuples</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">lags</span><span class="p">):</span>
<span class="n">flr</span> <span class="o">=</span> <span class="n">MVFLR</span><span class="o">.</span><span class="n">FLR</span><span class="p">()</span>
<span class="k">for</span> <span class="n">var</span><span class="p">,</span> <span class="n">fset</span> <span class="ow">in</span> <span class="n">path</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">flr</span><span class="o">.</span><span class="n">set_lhs</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">fset</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
<span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
<span class="k">return</span> <span class="n">flrs</span></div>
<div class="viewcode-block" id="MVFTS.generate_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)):</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="p">)</span>
<span class="n">tmp_flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
<span class="n">target_ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span>
<span class="n">target_point</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">][</span><span class="n">target_ix</span><span class="p">]</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">target_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="p">)</span>
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">tmp_flrs</span><span class="p">:</span>
<span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">target</span><span class="p">:</span>
<span class="n">flr</span><span class="o">.</span><span class="n">set_rhs</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
<span class="k">return</span> <span class="n">flrs</span></div>
<div class="viewcode-block" id="MVFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
<span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
<div class="viewcode-block" id="MVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.train">[docs]</a> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrs</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
<div class="viewcode-block" id="MVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast">[docs]</a> <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
<span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
<span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">mps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
<span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
<span class="c1">#Naïve approach is applied when no rules were found</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
<span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
<span class="n">mp</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">centroid</span>
<span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
<span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
<span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
<span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
<span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
<span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mps</span><span class="p">)</span>
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span><span class="n">mp</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">mv</span><span class="p">))</span>
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
<span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="MVFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'generators'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
<span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'You must provide parameter </span><span class="se">\'</span><span class="s1">generators</span><span class="se">\'</span><span class="s1">! generators is a dict where the keys'</span> <span class="o">+</span>
<span class="s1">' are the dataframe column names (except the target_variable) and the values are '</span> <span class="o">+</span>
<span class="s1">'lambda functions that accept one value (the actual value of the variable) '</span>
<span class="s1">' and return the next value or trained FTS models that accept the actual values and '</span>
<span class="s1">'forecast new ones.'</span><span class="p">)</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:]</span>
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
<span class="n">new_data_point</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="n">sample</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">]]]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">new_data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="MVFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
<span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
<span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">ups</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">los</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
<span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
<span class="c1">#Naïve approach is applied when no rules were found</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
<span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
<span class="n">up</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">upper</span>
<span class="n">lo</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">lower</span>
<span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
<span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">up</span><span class="p">)</span>
<span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
<span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
<span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
<span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
<span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
<span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
<span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ups</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
<span class="n">lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">los</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">])</span>
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
<span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="MVFTS.clone_parameters"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.clone_parameters">[docs]</a> <span class="k">def</span> <span class="nf">clone_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">clone_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">explanatory_variables</span>
<span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">target_variable</span></div>
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">":</span><span class="se">\n</span><span class="s2">"</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="n">_str</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span>
<span class="k">return</span> <span class="n">_str</span></div>
</pre></div>
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