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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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6
html/bayesnet/feature_selection/CFS.cc.func-c.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>
@@ -65,14 +65,14 @@
<tr>
<td class="coverFn"><a href="CFS.cc.gcov.html#L9">_ZN8bayesnet3CFS3fitEv</a></td>
<td class="coverFnHi">6</td>
<td class="coverFnHi">70</td>
</tr>
<tr>
<td class="coverFn"><a href="CFS.cc.gcov.html#L43">_ZN8bayesnet3CFS24computeContinueConditionERKSt6vectorIiSaIiEE</a></td>
<td class="coverFnHi">28</td>
<td class="coverFnHi">328</td>
</tr>

6
html/bayesnet/feature_selection/CFS.cc.func.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>
@@ -65,14 +65,14 @@
<tr>
<td class="coverFn"><a href="CFS.cc.gcov.html#L43">_ZN8bayesnet3CFS24computeContinueConditionERKSt6vectorIiSaIiEE</a></td>
<td class="coverFnHi">28</td>
<td class="coverFnHi">328</td>
</tr>
<tr>
<td class="coverFn"><a href="CFS.cc.gcov.html#L9">_ZN8bayesnet3CFS3fitEv</a></td>
<td class="coverFnHi">6</td>
<td class="coverFnHi">70</td>
</tr>

90
html/bayesnet/feature_selection/CFS.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>
@@ -70,46 +70,46 @@
<span id="L8"><span class="lineNum"> 8</span> : #include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span>
<span id="L9"><span class="lineNum"> 9</span> : #include &quot;CFS.h&quot;</span>
<span id="L10"><span class="lineNum"> 10</span> : namespace bayesnet {</span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 6 : void CFS::fit()</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 70 : void CFS::fit()</span></span>
<span id="L12"><span class="lineNum"> 12</span> : {</span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 6 : initialize();</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 6 : computeSuLabels();</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 6 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 6 : auto continueCondition = true;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 6 : auto feature = featureOrder[0];</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 6 : selectedFeatures.push_back(feature);</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 6 : selectedScores.push_back(suLabels[feature]);</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 6 : featureOrder.erase(featureOrder.begin());</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 34 : while (continueCondition) {</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 28 : double merit = std::numeric_limits&lt;double&gt;::lowest();</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 28 : int bestFeature = -1;</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 164 : for (auto feature : featureOrder) {</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 136 : selectedFeatures.push_back(feature);</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 70 : initialize();</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 70 : computeSuLabels();</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 70 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 70 : auto continueCondition = true;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 70 : auto feature = featureOrder[0];</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 70 : selectedFeatures.push_back(feature);</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 70 : selectedScores.push_back(suLabels[feature]);</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 70 : featureOrder.erase(featureOrder.begin());</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 398 : while (continueCondition) {</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 328 : double merit = std::numeric_limits&lt;double&gt;::lowest();</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 328 : int bestFeature = -1;</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 1929 : for (auto feature : featureOrder) {</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 1601 : selectedFeatures.push_back(feature);</span></span>
<span id="L26"><span class="lineNum"> 26</span> : // Compute merit with selectedFeatures</span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 136 : auto meritNew = computeMeritCFS();</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 136 : if (meritNew &gt; merit) {</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 57 : merit = meritNew;</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 57 : bestFeature = feature;</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 1601 : auto meritNew = computeMeritCFS();</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 1601 : if (meritNew &gt; merit) {</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 663 : merit = meritNew;</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 663 : bestFeature = feature;</span></span>
<span id="L31"><span class="lineNum"> 31</span> : }</span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 136 : selectedFeatures.pop_back();</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 1601 : selectedFeatures.pop_back();</span></span>
<span id="L33"><span class="lineNum"> 33</span> : }</span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 28 : if (bestFeature == -1) {</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 328 : if (bestFeature == -1) {</span></span>
<span id="L35"><span class="lineNum"> 35</span> : // meritNew has to be nan due to constant features</span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaUNC tlaBgUNC"> 0 : break;</span></span>
<span id="L37"><span class="lineNum"> 37</span> : }</span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC tlaBgGNC"> 28 : selectedFeatures.push_back(bestFeature);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 28 : selectedScores.push_back(merit);</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 28 : featureOrder.erase(remove(featureOrder.begin(), featureOrder.end(), bestFeature), featureOrder.end());</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 28 : continueCondition = computeContinueCondition(featureOrder);</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC tlaBgGNC"> 328 : selectedFeatures.push_back(bestFeature);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 328 : selectedScores.push_back(merit);</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 328 : featureOrder.erase(remove(featureOrder.begin(), featureOrder.end(), bestFeature), featureOrder.end());</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 328 : continueCondition = computeContinueCondition(featureOrder);</span></span>
<span id="L42"><span class="lineNum"> 42</span> : }</span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 6 : fitted = true;</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 6 : }</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 28 : bool CFS::computeContinueCondition(const std::vector&lt;int&gt;&amp; featureOrder)</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 70 : fitted = true;</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 70 : }</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 328 : bool CFS::computeContinueCondition(const std::vector&lt;int&gt;&amp; featureOrder)</span></span>
<span id="L46"><span class="lineNum"> 46</span> : {</span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 28 : if (selectedFeatures.size() == maxFeatures || featureOrder.size() == 0) {</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 1 : return false;</span></span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 328 : if (selectedFeatures.size() == maxFeatures || featureOrder.size() == 0) {</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 11 : return false;</span></span>
<span id="L49"><span class="lineNum"> 49</span> : }</span>
<span id="L50"><span class="lineNum"> 50</span> <span class="tlaGNC"> 27 : if (selectedScores.size() &gt;= 5) {</span></span>
<span id="L50"><span class="lineNum"> 50</span> <span class="tlaGNC"> 317 : if (selectedScores.size() &gt;= 5) {</span></span>
<span id="L51"><span class="lineNum"> 51</span> : /*</span>
<span id="L52"><span class="lineNum"> 52</span> : &quot;To prevent the best first search from exploring the entire</span>
<span id="L53"><span class="lineNum"> 53</span> : feature subset search space, a stopping criterion is imposed.</span>
@@ -117,25 +117,25 @@
<span id="L55"><span class="lineNum"> 55</span> : subsets show no improvement over the current best subset.&quot;</span>
<span id="L56"><span class="lineNum"> 56</span> : as stated in Mark A.Hall Thesis</span>
<span id="L57"><span class="lineNum"> 57</span> : */</span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 10 : double item_ant = std::numeric_limits&lt;double&gt;::lowest();</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 10 : int num = 0;</span></span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 10 : std::vector&lt;double&gt; lastFive(selectedScores.end() - 5, selectedScores.end());</span></span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 40 : for (auto item : lastFive) {</span></span>
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 35 : if (item_ant == std::numeric_limits&lt;double&gt;::lowest()) {</span></span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 10 : item_ant = item;</span></span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 118 : double item_ant = std::numeric_limits&lt;double&gt;::lowest();</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 118 : int num = 0;</span></span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 118 : std::vector&lt;double&gt; lastFive(selectedScores.end() - 5, selectedScores.end());</span></span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 472 : for (auto item : lastFive) {</span></span>
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 413 : if (item_ant == std::numeric_limits&lt;double&gt;::lowest()) {</span></span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 118 : item_ant = item;</span></span>
<span id="L64"><span class="lineNum"> 64</span> : }</span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 35 : if (item &gt; item_ant) {</span></span>
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 5 : break;</span></span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 413 : if (item &gt; item_ant) {</span></span>
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 59 : break;</span></span>
<span id="L67"><span class="lineNum"> 67</span> : } else {</span>
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 30 : num++;</span></span>
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 30 : item_ant = item;</span></span>
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 354 : num++;</span></span>
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 354 : item_ant = item;</span></span>
<span id="L70"><span class="lineNum"> 70</span> : }</span>
<span id="L71"><span class="lineNum"> 71</span> : }</span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 10 : if (num == 5) {</span></span>
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 5 : return false;</span></span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 118 : if (num == 5) {</span></span>
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 59 : return false;</span></span>
<span id="L74"><span class="lineNum"> 74</span> : }</span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 10 : }</span></span>
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 22 : return true;</span></span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 118 : }</span></span>
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 258 : return true;</span></span>
<span id="L77"><span class="lineNum"> 77</span> : }</span>
<span id="L78"><span class="lineNum"> 78</span> : }</span>
</pre>

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html/bayesnet/feature_selection/CFS.h.func-c.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>
@@ -72,21 +72,21 @@
<tr>
<td class="coverFn"><a href="CFS.h.gcov.html#L18">_ZN8bayesnet3CFSD0Ev</a></td>
<td class="coverFnHi">12</td>
<td class="coverFnHi">140</td>
</tr>
<tr>
<td class="coverFnAlias"><a href="CFS.h.gcov.html#L18">_ZN8bayesnet3CFSD0Ev</a></td>
<td class="coverFnAliasHi">6</td>
<td class="coverFnAliasHi">70</td>
</tr>
<tr>
<td class="coverFnAlias"><a href="CFS.h.gcov.html#L18">_ZN8bayesnet3CFSD2Ev</a></td>
<td class="coverFnAliasHi">6</td>
<td class="coverFnAliasHi">70</td>
</tr>

8
html/bayesnet/feature_selection/CFS.h.func.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>
@@ -72,21 +72,21 @@
<tr>
<td class="coverFn"><a href="CFS.h.gcov.html#L18">_ZN8bayesnet3CFSD0Ev</a></td>
<td class="coverFnHi">12</td>
<td class="coverFnHi">140</td>
</tr>
<tr>
<td class="coverFnAlias"><a href="CFS.h.gcov.html#L18">_ZN8bayesnet3CFSD0Ev</a></td>
<td class="coverFnAliasHi">6</td>
<td class="coverFnAliasHi">70</td>
</tr>
<tr>
<td class="coverFnAlias"><a href="CFS.h.gcov.html#L18">_ZN8bayesnet3CFSD2Ev</a></td>
<td class="coverFnAliasHi">6</td>
<td class="coverFnAliasHi">70</td>
</tr>

4
html/bayesnet/feature_selection/CFS.h.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>
@@ -79,7 +79,7 @@
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 6 : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights)</span></span>
<span id="L18"><span class="lineNum"> 18</span> : {</span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 6 : }</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 12 : virtual ~CFS() {};</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 140 : virtual ~CFS() {};</span></span>
<span id="L21"><span class="lineNum"> 21</span> : void fit() override;</span>
<span id="L22"><span class="lineNum"> 22</span> : private:</span>
<span id="L23"><span class="lineNum"> 23</span> : bool computeContinueCondition(const std::vector&lt;int&gt;&amp; featureOrder);</span>

6
html/bayesnet/feature_selection/FCBF.cc.func-c.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>
@@ -65,14 +65,14 @@
<tr>
<td class="coverFn"><a href="FCBF.cc.gcov.html#L16">_ZN8bayesnet4FCBF3fitEv</a></td>
<td class="coverFnHi">5</td>
<td class="coverFnHi">56</td>
</tr>
<tr>
<td class="coverFn"><a href="FCBF.cc.gcov.html#L9">_ZN8bayesnet4FCBFC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_iiS4_d</a></td>
<td class="coverFnHi">7</td>
<td class="coverFnHi">78</td>
</tr>

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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>
@@ -65,14 +65,14 @@
<tr>
<td class="coverFn"><a href="FCBF.cc.gcov.html#L16">_ZN8bayesnet4FCBF3fitEv</a></td>
<td class="coverFnHi">5</td>
<td class="coverFnHi">56</td>
</tr>
<tr>
<td class="coverFn"><a href="FCBF.cc.gcov.html#L9">_ZN8bayesnet4FCBFC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_iiS4_d</a></td>
<td class="coverFnHi">7</td>
<td class="coverFnHi">78</td>
</tr>

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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>
@@ -70,45 +70,45 @@
<span id="L8"><span class="lineNum"> 8</span> : #include &quot;FCBF.h&quot;</span>
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
<span id="L10"><span class="lineNum"> 10</span> : </span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 7 : FCBF::FCBF(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int maxFeatures, const int classNumStates, const torch::Tensor&amp; weights, const double threshold) :</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 7 : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 78 : FCBF::FCBF(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int maxFeatures, const int classNumStates, const torch::Tensor&amp; weights, const double threshold) :</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 78 : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)</span></span>
<span id="L13"><span class="lineNum"> 13</span> : {</span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 7 : if (threshold &lt; 1e-7) {</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 2 : throw std::invalid_argument(&quot;Threshold cannot be less than 1e-7&quot;);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 78 : if (threshold &lt; 1e-7) {</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 22 : throw std::invalid_argument(&quot;Threshold cannot be less than 1e-7&quot;);</span></span>
<span id="L16"><span class="lineNum"> 16</span> : }</span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 7 : }</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 5 : void FCBF::fit()</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 78 : }</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 56 : void FCBF::fit()</span></span>
<span id="L19"><span class="lineNum"> 19</span> : {</span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 5 : initialize();</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 5 : computeSuLabels();</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 5 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 5 : auto featureOrderCopy = featureOrder;</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 42 : for (const auto&amp; feature : featureOrder) {</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 56 : initialize();</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 56 : computeSuLabels();</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 56 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 56 : auto featureOrderCopy = featureOrder;</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 472 : for (const auto&amp; feature : featureOrder) {</span></span>
<span id="L25"><span class="lineNum"> 25</span> : // Don't self compare</span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 37 : featureOrderCopy.erase(featureOrderCopy.begin());</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 37 : if (suLabels.at(feature) == 0.0) {</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 416 : featureOrderCopy.erase(featureOrderCopy.begin());</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 416 : if (suLabels.at(feature) == 0.0) {</span></span>
<span id="L28"><span class="lineNum"> 28</span> : // The feature has been removed from the list</span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 16 : continue;</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 180 : continue;</span></span>
<span id="L30"><span class="lineNum"> 30</span> : }</span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 21 : if (suLabels.at(feature) &lt; threshold) {</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 236 : if (suLabels.at(feature) &lt; threshold) {</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaUNC tlaBgUNC"> 0 : break;</span></span>
<span id="L33"><span class="lineNum"> 33</span> : }</span>
<span id="L34"><span class="lineNum"> 34</span> : // Remove redundant features</span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC tlaBgGNC"> 116 : for (const auto&amp; featureCopy : featureOrderCopy) {</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 95 : double value = computeSuFeatures(feature, featureCopy);</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 95 : if (value &gt;= suLabels.at(featureCopy)) {</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC tlaBgGNC"> 1307 : for (const auto&amp; featureCopy : featureOrderCopy) {</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 1071 : double value = computeSuFeatures(feature, featureCopy);</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 1071 : if (value &gt;= suLabels.at(featureCopy)) {</span></span>
<span id="L38"><span class="lineNum"> 38</span> : // Remove feature from list</span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 33 : suLabels[featureCopy] = 0.0;</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 373 : suLabels[featureCopy] = 0.0;</span></span>
<span id="L40"><span class="lineNum"> 40</span> : }</span>
<span id="L41"><span class="lineNum"> 41</span> : }</span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 21 : selectedFeatures.push_back(feature);</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 21 : selectedScores.push_back(suLabels[feature]);</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 21 : if (selectedFeatures.size() == maxFeatures) {</span></span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 236 : selectedFeatures.push_back(feature);</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 236 : selectedScores.push_back(suLabels[feature]);</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 236 : if (selectedFeatures.size() == maxFeatures) {</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaUNC tlaBgUNC"> 0 : break;</span></span>
<span id="L46"><span class="lineNum"> 46</span> : }</span>
<span id="L47"><span class="lineNum"> 47</span> : }</span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC tlaBgGNC"> 5 : fitted = true;</span></span>
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 5 : }</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC tlaBgGNC"> 56 : fitted = true;</span></span>
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 56 : }</span></span>
<span id="L50"><span class="lineNum"> 50</span> : }</span>
</pre>
</td>

2
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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>

2
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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>

2
html/bayesnet/feature_selection/FCBF.h.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>

18
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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>
@@ -65,56 +65,56 @@
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L14">_ZN8bayesnet13FeatureSelect10initializeEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L34">_ZN8bayesnet13FeatureSelect15computeSuLabelsEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L68">_ZNK8bayesnet13FeatureSelect11getFeaturesEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L75">_ZNK8bayesnet13FeatureSelect9getScoresEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L9">_ZN8bayesnet13FeatureSelectC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_iiS4_</a></td>
<td class="coverFnHi">22</td>
<td class="coverFnHi">250</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L55">_ZN8bayesnet13FeatureSelect15computeMeritCFSEv</a></td>
<td class="coverFnHi">158</td>
<td class="coverFnHi">1851</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L19">_ZN8bayesnet13FeatureSelect22symmetricalUncertaintyEii</a></td>
<td class="coverFnHi">411</td>
<td class="coverFnHi">5151</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L42">_ZN8bayesnet13FeatureSelect17computeSuFeaturesEii</a></td>
<td class="coverFnHi">980</td>
<td class="coverFnHi">11429</td>
</tr>

18
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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>
@@ -65,56 +65,56 @@
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L14">_ZN8bayesnet13FeatureSelect10initializeEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L55">_ZN8bayesnet13FeatureSelect15computeMeritCFSEv</a></td>
<td class="coverFnHi">158</td>
<td class="coverFnHi">1851</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L34">_ZN8bayesnet13FeatureSelect15computeSuLabelsEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L42">_ZN8bayesnet13FeatureSelect17computeSuFeaturesEii</a></td>
<td class="coverFnHi">980</td>
<td class="coverFnHi">11429</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L19">_ZN8bayesnet13FeatureSelect22symmetricalUncertaintyEii</a></td>
<td class="coverFnHi">411</td>
<td class="coverFnHi">5151</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L9">_ZN8bayesnet13FeatureSelectC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_iiS4_</a></td>
<td class="coverFnHi">22</td>
<td class="coverFnHi">250</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L68">_ZNK8bayesnet13FeatureSelect11getFeaturesEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>
<tr>
<td class="coverFn"><a href="FeatureSelect.cc.gcov.html#L75">_ZNK8bayesnet13FeatureSelect9getScoresEv</a></td>
<td class="coverFnHi">16</td>
<td class="coverFnHi">184</td>
</tr>

86
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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>
@@ -70,17 +70,17 @@
<span id="L8"><span class="lineNum"> 8</span> : #include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span>
<span id="L9"><span class="lineNum"> 9</span> : #include &quot;FeatureSelect.h&quot;</span>
<span id="L10"><span class="lineNum"> 10</span> : namespace bayesnet {</span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 22 : FeatureSelect::FeatureSelect(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int maxFeatures, const int classNumStates, const torch::Tensor&amp; weights) :</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 22 : Metrics(samples, features, className, classNumStates), maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures), weights(weights)</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 250 : FeatureSelect::FeatureSelect(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int maxFeatures, const int classNumStates, const torch::Tensor&amp; weights) :</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 250 : Metrics(samples, features, className, classNumStates), maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures), weights(weights)</span></span>
<span id="L13"><span class="lineNum"> 13</span> : </span>
<span id="L14"><span class="lineNum"> 14</span> : {</span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 22 : }</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 16 : void FeatureSelect::initialize()</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 250 : }</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 184 : void FeatureSelect::initialize()</span></span>
<span id="L17"><span class="lineNum"> 17</span> : {</span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 16 : selectedFeatures.clear();</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 16 : selectedScores.clear();</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 16 : }</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 411 : double FeatureSelect::symmetricalUncertainty(int a, int b)</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 184 : selectedFeatures.clear();</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 184 : selectedScores.clear();</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 184 : }</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 5151 : double FeatureSelect::symmetricalUncertainty(int a, int b)</span></span>
<span id="L22"><span class="lineNum"> 22</span> : {</span>
<span id="L23"><span class="lineNum"> 23</span> : /*</span>
<span id="L24"><span class="lineNum"> 24</span> : Compute symmetrical uncertainty. Normalize* information gain (mutual</span>
@@ -88,60 +88,60 @@
<span id="L26"><span class="lineNum"> 26</span> : the bias due to high cardinality features. *Range [0, 1]</span>
<span id="L27"><span class="lineNum"> 27</span> : (https://www.sciencedirect.com/science/article/pii/S0020025519303603)</span>
<span id="L28"><span class="lineNum"> 28</span> : */</span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 1233 : auto x = samples.index({ a, &quot;...&quot; });</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 1233 : auto y = samples.index({ b, &quot;...&quot; });</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 411 : auto mu = mutualInformation(x, y, weights);</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 411 : auto hx = entropy(x, weights);</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 411 : auto hy = entropy(y, weights);</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 411 : return 2.0 * mu / (hx + hy);</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 1233 : }</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 16 : void FeatureSelect::computeSuLabels()</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 15453 : auto x = samples.index({ a, &quot;...&quot; });</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 15453 : auto y = samples.index({ b, &quot;...&quot; });</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 5151 : auto mu = mutualInformation(x, y, weights);</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 5151 : auto hx = entropy(x, weights);</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 5151 : auto hy = entropy(y, weights);</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 5151 : return 2.0 * mu / (hx + hy);</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 15453 : }</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 184 : void FeatureSelect::computeSuLabels()</span></span>
<span id="L37"><span class="lineNum"> 37</span> : {</span>
<span id="L38"><span class="lineNum"> 38</span> : // Compute Simmetrical Uncertainty between features and labels</span>
<span id="L39"><span class="lineNum"> 39</span> : // https://en.wikipedia.org/wiki/Symmetric_uncertainty</span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 135 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 119 : suLabels.push_back(symmetricalUncertainty(i, -1));</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 1976 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 1792 : suLabels.push_back(symmetricalUncertainty(i, -1));</span></span>
<span id="L42"><span class="lineNum"> 42</span> : }</span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 16 : }</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 980 : double FeatureSelect::computeSuFeatures(const int firstFeature, const int secondFeature)</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 184 : }</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 11429 : double FeatureSelect::computeSuFeatures(const int firstFeature, const int secondFeature)</span></span>
<span id="L45"><span class="lineNum"> 45</span> : {</span>
<span id="L46"><span class="lineNum"> 46</span> : // Compute Simmetrical Uncertainty between features</span>
<span id="L47"><span class="lineNum"> 47</span> : // https://en.wikipedia.org/wiki/Symmetric_uncertainty</span>
<span id="L48"><span class="lineNum"> 48</span> : try {</span>
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 980 : return suFeatures.at({ firstFeature, secondFeature });</span></span>
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 11429 : return suFeatures.at({ firstFeature, secondFeature });</span></span>
<span id="L50"><span class="lineNum"> 50</span> : }</span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 292 : catch (const std::out_of_range&amp; e) {</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 292 : double result = symmetricalUncertainty(firstFeature, secondFeature);</span></span>
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 292 : suFeatures[{firstFeature, secondFeature}] = result;</span></span>
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 292 : return result;</span></span>
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 292 : }</span></span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 3359 : catch (const std::out_of_range&amp; e) {</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 3359 : double result = symmetricalUncertainty(firstFeature, secondFeature);</span></span>
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 3359 : suFeatures[{firstFeature, secondFeature}] = result;</span></span>
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 3359 : return result;</span></span>
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 3359 : }</span></span>
<span id="L56"><span class="lineNum"> 56</span> : }</span>
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 158 : double FeatureSelect::computeMeritCFS()</span></span>
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 1851 : double FeatureSelect::computeMeritCFS()</span></span>
<span id="L58"><span class="lineNum"> 58</span> : {</span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 158 : double rcf = 0;</span></span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 727 : for (auto feature : selectedFeatures) {</span></span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 569 : rcf += suLabels[feature];</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 1851 : double rcf = 0;</span></span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 8514 : for (auto feature : selectedFeatures) {</span></span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 6663 : rcf += suLabels[feature];</span></span>
<span id="L62"><span class="lineNum"> 62</span> : }</span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 158 : double rff = 0;</span></span>
<span id="L64"><span class="lineNum"> 64</span> <span class="tlaGNC"> 158 : int n = selectedFeatures.size();</span></span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 1043 : for (const auto&amp; item : doCombinations(selectedFeatures)) {</span></span>
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 885 : rff += computeSuFeatures(item.first, item.second);</span></span>
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 158 : }</span></span>
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 158 : return rcf / sqrt(n + (n * n - n) * rff);</span></span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 1851 : double rff = 0;</span></span>
<span id="L64"><span class="lineNum"> 64</span> <span class="tlaGNC"> 1851 : int n = selectedFeatures.size();</span></span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 12209 : for (const auto&amp; item : doCombinations(selectedFeatures)) {</span></span>
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 10358 : rff += computeSuFeatures(item.first, item.second);</span></span>
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 1851 : }</span></span>
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 1851 : return rcf / sqrt(n + (n * n - n) * rff);</span></span>
<span id="L69"><span class="lineNum"> 69</span> : }</span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 16 : std::vector&lt;int&gt; FeatureSelect::getFeatures() const</span></span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 184 : std::vector&lt;int&gt; FeatureSelect::getFeatures() const</span></span>
<span id="L71"><span class="lineNum"> 71</span> : {</span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 16 : if (!fitted) {</span></span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 184 : if (!fitted) {</span></span>
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaUNC tlaBgUNC"> 0 : throw std::runtime_error(&quot;FeatureSelect not fitted&quot;);</span></span>
<span id="L74"><span class="lineNum"> 74</span> : }</span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC tlaBgGNC"> 16 : return selectedFeatures;</span></span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC tlaBgGNC"> 184 : return selectedFeatures;</span></span>
<span id="L76"><span class="lineNum"> 76</span> : }</span>
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 16 : std::vector&lt;double&gt; FeatureSelect::getScores() const</span></span>
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 184 : std::vector&lt;double&gt; FeatureSelect::getScores() const</span></span>
<span id="L78"><span class="lineNum"> 78</span> : {</span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 16 : if (!fitted) {</span></span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 184 : if (!fitted) {</span></span>
<span id="L80"><span class="lineNum"> 80</span> <span class="tlaUNC tlaBgUNC"> 0 : throw std::runtime_error(&quot;FeatureSelect not fitted&quot;);</span></span>
<span id="L81"><span class="lineNum"> 81</span> : }</span>
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC tlaBgGNC"> 16 : return selectedScores;</span></span>
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC tlaBgGNC"> 184 : return selectedScores;</span></span>
<span id="L83"><span class="lineNum"> 83</span> : }</span>
<span id="L84"><span class="lineNum"> 84</span> : }</span>
</pre>

2
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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="headerCovTableEntryLo">50.0&nbsp;%</td>

2
html/bayesnet/feature_selection/FeatureSelect.h.func.html
View File

@@ -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="headerCovTableEntryLo">50.0&nbsp;%</td>

2
html/bayesnet/feature_selection/FeatureSelect.h.gcov.html
View File

@@ -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="headerCovTableEntryLo">50.0&nbsp;%</td>

6
html/bayesnet/feature_selection/IWSS.cc.func-c.html
View File

@@ -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>
@@ -65,14 +65,14 @@
<tr>
<td class="coverFn"><a href="IWSS.cc.gcov.html#L16">_ZN8bayesnet4IWSS3fitEv</a></td>
<td class="coverFnHi">5</td>
<td class="coverFnHi">58</td>
</tr>
<tr>
<td class="coverFn"><a href="IWSS.cc.gcov.html#L9">_ZN8bayesnet4IWSSC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_iiS4_d</a></td>
<td class="coverFnHi">9</td>
<td class="coverFnHi">102</td>
</tr>

6
html/bayesnet/feature_selection/IWSS.cc.func.html
View File

@@ -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>
@@ -65,14 +65,14 @@
<tr>
<td class="coverFn"><a href="IWSS.cc.gcov.html#L16">_ZN8bayesnet4IWSS3fitEv</a></td>
<td class="coverFnHi">5</td>
<td class="coverFnHi">58</td>
</tr>
<tr>
<td class="coverFn"><a href="IWSS.cc.gcov.html#L9">_ZN8bayesnet4IWSSC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_iiS4_d</a></td>
<td class="coverFnHi">9</td>
<td class="coverFnHi">102</td>
</tr>

58
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View File

@@ -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>
@@ -70,48 +70,48 @@
<span id="L8"><span class="lineNum"> 8</span> : #include &quot;bayesnet/utils/bayesnetUtils.h&quot;</span>
<span id="L9"><span class="lineNum"> 9</span> : #include &quot;IWSS.h&quot;</span>
<span id="L10"><span class="lineNum"> 10</span> : namespace bayesnet {</span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 9 : IWSS::IWSS(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int maxFeatures, const int classNumStates, const torch::Tensor&amp; weights, const double threshold) :</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 9 : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 102 : IWSS::IWSS(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int maxFeatures, const int classNumStates, const torch::Tensor&amp; weights, const double threshold) :</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 102 : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)</span></span>
<span id="L13"><span class="lineNum"> 13</span> : {</span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 9 : if (threshold &lt; 0 || threshold &gt; .5) {</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 4 : throw std::invalid_argument(&quot;Threshold has to be in [0, 0.5]&quot;);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 102 : if (threshold &lt; 0 || threshold &gt; .5) {</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 44 : throw std::invalid_argument(&quot;Threshold has to be in [0, 0.5]&quot;);</span></span>
<span id="L16"><span class="lineNum"> 16</span> : }</span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 9 : }</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 5 : void IWSS::fit()</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 102 : }</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 58 : void IWSS::fit()</span></span>
<span id="L19"><span class="lineNum"> 19</span> : {</span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 5 : initialize();</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 5 : computeSuLabels();</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 5 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 5 : auto featureOrderCopy = featureOrder;</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 58 : initialize();</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 58 : computeSuLabels();</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 58 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 58 : auto featureOrderCopy = featureOrder;</span></span>
<span id="L24"><span class="lineNum"> 24</span> : // Add first and second features to result</span>
<span id="L25"><span class="lineNum"> 25</span> : // First with its own score</span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 5 : auto first_feature = pop_first(featureOrderCopy);</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 5 : selectedFeatures.push_back(first_feature);</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 5 : selectedScores.push_back(suLabels.at(first_feature));</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 58 : auto first_feature = pop_first(featureOrderCopy);</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 58 : selectedFeatures.push_back(first_feature);</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 58 : selectedScores.push_back(suLabels.at(first_feature));</span></span>
<span id="L29"><span class="lineNum"> 29</span> : // Second with the score of the candidates</span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 5 : selectedFeatures.push_back(pop_first(featureOrderCopy));</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 5 : auto merit = computeMeritCFS();</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 5 : selectedScores.push_back(merit);</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 17 : for (const auto feature : featureOrderCopy) {</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 17 : selectedFeatures.push_back(feature);</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 58 : selectedFeatures.push_back(pop_first(featureOrderCopy));</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 58 : auto merit = computeMeritCFS();</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 58 : selectedScores.push_back(merit);</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 192 : for (const auto feature : featureOrderCopy) {</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 192 : selectedFeatures.push_back(feature);</span></span>
<span id="L35"><span class="lineNum"> 35</span> : // Compute merit with selectedFeatures</span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 17 : auto meritNew = computeMeritCFS();</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 17 : double delta = merit != 0.0 ? std::abs(merit - meritNew) / merit : 0.0;</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 17 : if (meritNew &gt; merit || delta &lt; threshold) {</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 12 : if (meritNew &gt; merit) {</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 192 : auto meritNew = computeMeritCFS();</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 192 : double delta = merit != 0.0 ? std::abs(merit - meritNew) / merit : 0.0;</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 192 : if (meritNew &gt; merit || delta &lt; threshold) {</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 134 : if (meritNew &gt; merit) {</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaUNC tlaBgUNC"> 0 : merit = meritNew;</span></span>
<span id="L41"><span class="lineNum"> 41</span> : }</span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC tlaBgGNC"> 12 : selectedScores.push_back(meritNew);</span></span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC tlaBgGNC"> 134 : selectedScores.push_back(meritNew);</span></span>
<span id="L43"><span class="lineNum"> 43</span> : } else {</span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 5 : selectedFeatures.pop_back();</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 5 : break;</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 58 : selectedFeatures.pop_back();</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 58 : break;</span></span>
<span id="L46"><span class="lineNum"> 46</span> : }</span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 12 : if (selectedFeatures.size() == maxFeatures) {</span></span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 134 : if (selectedFeatures.size() == maxFeatures) {</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaUNC tlaBgUNC"> 0 : break;</span></span>
<span id="L49"><span class="lineNum"> 49</span> : }</span>
<span id="L50"><span class="lineNum"> 50</span> : }</span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC tlaBgGNC"> 5 : fitted = true;</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 5 : }</span></span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC tlaBgGNC"> 58 : fitted = true;</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 58 : }</span></span>
<span id="L53"><span class="lineNum"> 53</span> : }</span>
</pre>
</td>

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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>

2
html/bayesnet/feature_selection/IWSS.h.func.html
View File

@@ -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>

2
html/bayesnet/feature_selection/IWSS.h.gcov.html
View File

@@ -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>

2
html/bayesnet/feature_selection/index-sort-f.html
View File

@@ -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">95.7&nbsp;%</td>

2
html/bayesnet/feature_selection/index-sort-l.html
View File

@@ -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">95.7&nbsp;%</td>

2
html/bayesnet/feature_selection/index.html
View File

@@ -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">95.7&nbsp;%</td>