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Add hyperparameter convergence_best

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move test libraries to test folder
This commit is contained in:
Ricardo Montañana Gómez
2024-04-30 00:52:09 +02:00
parent f014928411
commit ae469b8146
721 changed files with 206095 additions and 2496 deletions
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html/bayesnet/classifiers/SPODELd.cc.gcov.html
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@@ -37,7 +37,7 @@
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 17:30:26</td>
<td class="headerValue">2024-04-29 20:48:03</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -69,45 +69,45 @@
<span id="L7"><span class="lineNum"> 7</span> : #include &quot;SPODELd.h&quot;</span>
<span id="L8"><span class="lineNum"> 8</span> : </span>
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 55 : SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 42 : SPODELd&amp; SPODELd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 605 : SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 462 : SPODELd&amp; SPODELd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L12"><span class="lineNum"> 12</span> : {</span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 42 : checkInput(X_, y_);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 42 : Xf = X_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 42 : y = y_;</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 42 : return commonFit(features_, className_, states_);</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 462 : checkInput(X_, y_);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 462 : Xf = X_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 462 : y = y_;</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 462 : return commonFit(features_, className_, states_);</span></span>
<span id="L17"><span class="lineNum"> 17</span> : }</span>
<span id="L18"><span class="lineNum"> 18</span> : </span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 2 : SPODELd&amp; SPODELd::fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 22 : SPODELd&amp; SPODELd::fit(torch::Tensor&amp; dataset, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L20"><span class="lineNum"> 20</span> : {</span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 2 : if (!torch::is_floating_point(dataset)) {</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 1 : throw std::runtime_error(&quot;Dataset must be a floating point tensor&quot;);</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 22 : if (!torch::is_floating_point(dataset)) {</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 11 : throw std::runtime_error(&quot;Dataset must be a floating point tensor&quot;);</span></span>
<span id="L23"><span class="lineNum"> 23</span> : }</span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 4 : Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), &quot;...&quot; }).clone();</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 3 : y = dataset.index({ -1, &quot;...&quot; }).clone().to(torch::kInt32);</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 1 : return commonFit(features_, className_, states_);</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 3 : }</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 44 : Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), &quot;...&quot; }).clone();</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 33 : y = dataset.index({ -1, &quot;...&quot; }).clone().to(torch::kInt32);</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 11 : return commonFit(features_, className_, states_);</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 33 : }</span></span>
<span id="L28"><span class="lineNum"> 28</span> : </span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 43 : SPODELd&amp; SPODELd::commonFit(const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 473 : SPODELd&amp; SPODELd::commonFit(const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L30"><span class="lineNum"> 30</span> : {</span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 43 : features = features_;</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 43 : className = className_;</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 473 : features = features_;</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 473 : className = className_;</span></span>
<span id="L33"><span class="lineNum"> 33</span> : // Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 43 : states = fit_local_discretization(y);</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 473 : states = fit_local_discretization(y);</span></span>
<span id="L35"><span class="lineNum"> 35</span> : // We have discretized the input data</span>
<span id="L36"><span class="lineNum"> 36</span> : // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network</span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 43 : SPODE::fit(dataset, features, className, states);</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 43 : states = localDiscretizationProposal(states, model);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 43 : return *this;</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 473 : SPODE::fit(dataset, features, className, states);</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 473 : states = localDiscretizationProposal(states, model);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 473 : return *this;</span></span>
<span id="L40"><span class="lineNum"> 40</span> : }</span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 34 : torch::Tensor SPODELd::predict(torch::Tensor&amp; X)</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 374 : torch::Tensor SPODELd::predict(torch::Tensor&amp; X)</span></span>
<span id="L42"><span class="lineNum"> 42</span> : {</span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 34 : auto Xt = prepareX(X);</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 68 : return SPODE::predict(Xt);</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 34 : }</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 9 : std::vector&lt;std::string&gt; SPODELd::graph(const std::string&amp; name) const</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 374 : auto Xt = prepareX(X);</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 748 : return SPODE::predict(Xt);</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 374 : }</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 99 : std::vector&lt;std::string&gt; SPODELd::graph(const std::string&amp; name) const</span></span>
<span id="L47"><span class="lineNum"> 47</span> : {</span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 9 : return SPODE::graph(name);</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 99 : return SPODE::graph(name);</span></span>
<span id="L49"><span class="lineNum"> 49</span> : }</span>
<span id="L50"><span class="lineNum"> 50</span> : }</span>
</pre>