Add hyperparameter convergence_best
move test libraries to test folder
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@@ -37,7 +37,7 @@
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</tr>
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<tr>
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<td class="headerItem">Test Date:</td>
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<td class="headerValue">2024-04-21 17:30:26</td>
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<td class="headerValue">2024-04-29 20:48:03</td>
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<td></td>
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<td class="headerItem">Functions:</td>
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<td class="headerCovTableEntryHi">100.0 %</td>
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@@ -70,48 +70,48 @@
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<span id="L8"><span class="lineNum"> 8</span> : #include "bayesnet/utils/bayesnetUtils.h"</span>
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<span id="L9"><span class="lineNum"> 9</span> : #include "IWSS.h"</span>
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<span id="L10"><span class="lineNum"> 10</span> : namespace bayesnet {</span>
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<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 9 : IWSS::IWSS(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold) :</span></span>
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<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 9 : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)</span></span>
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<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 102 : IWSS::IWSS(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold) :</span></span>
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<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 102 : FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)</span></span>
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<span id="L13"><span class="lineNum"> 13</span> : {</span>
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<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 9 : if (threshold < 0 || threshold > .5) {</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 4 : throw std::invalid_argument("Threshold has to be in [0, 0.5]");</span></span>
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<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 102 : if (threshold < 0 || threshold > .5) {</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 44 : throw std::invalid_argument("Threshold has to be in [0, 0.5]");</span></span>
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<span id="L16"><span class="lineNum"> 16</span> : }</span>
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<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 9 : }</span></span>
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<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 5 : void IWSS::fit()</span></span>
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<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 102 : }</span></span>
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<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 58 : void IWSS::fit()</span></span>
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<span id="L19"><span class="lineNum"> 19</span> : {</span>
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<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 5 : initialize();</span></span>
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<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 5 : computeSuLabels();</span></span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 5 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 5 : auto featureOrderCopy = featureOrder;</span></span>
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<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 58 : initialize();</span></span>
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<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 58 : computeSuLabels();</span></span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 58 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 58 : auto featureOrderCopy = featureOrder;</span></span>
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<span id="L24"><span class="lineNum"> 24</span> : // Add first and second features to result</span>
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<span id="L25"><span class="lineNum"> 25</span> : // First with its own score</span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 5 : auto first_feature = pop_first(featureOrderCopy);</span></span>
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<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 5 : selectedFeatures.push_back(first_feature);</span></span>
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<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 5 : selectedScores.push_back(suLabels.at(first_feature));</span></span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 58 : auto first_feature = pop_first(featureOrderCopy);</span></span>
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<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 58 : selectedFeatures.push_back(first_feature);</span></span>
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<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 58 : selectedScores.push_back(suLabels.at(first_feature));</span></span>
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<span id="L29"><span class="lineNum"> 29</span> : // Second with the score of the candidates</span>
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<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 5 : selectedFeatures.push_back(pop_first(featureOrderCopy));</span></span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 5 : auto merit = computeMeritCFS();</span></span>
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<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 5 : selectedScores.push_back(merit);</span></span>
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<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 17 : for (const auto feature : featureOrderCopy) {</span></span>
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<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 17 : selectedFeatures.push_back(feature);</span></span>
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<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 58 : selectedFeatures.push_back(pop_first(featureOrderCopy));</span></span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 58 : auto merit = computeMeritCFS();</span></span>
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<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 58 : selectedScores.push_back(merit);</span></span>
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<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 192 : for (const auto feature : featureOrderCopy) {</span></span>
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<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 192 : selectedFeatures.push_back(feature);</span></span>
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<span id="L35"><span class="lineNum"> 35</span> : // Compute merit with selectedFeatures</span>
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<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 17 : auto meritNew = computeMeritCFS();</span></span>
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<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>
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<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 17 : if (meritNew > merit || delta < threshold) {</span></span>
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<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 12 : if (meritNew > merit) {</span></span>
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<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 192 : auto meritNew = computeMeritCFS();</span></span>
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<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>
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<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 192 : if (meritNew > merit || delta < threshold) {</span></span>
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<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 134 : if (meritNew > merit) {</span></span>
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<span id="L40"><span class="lineNum"> 40</span> <span class="tlaUNC tlaBgUNC"> 0 : merit = meritNew;</span></span>
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<span id="L41"><span class="lineNum"> 41</span> : }</span>
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<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC tlaBgGNC"> 12 : selectedScores.push_back(meritNew);</span></span>
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<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC tlaBgGNC"> 134 : selectedScores.push_back(meritNew);</span></span>
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<span id="L43"><span class="lineNum"> 43</span> : } else {</span>
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<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 5 : selectedFeatures.pop_back();</span></span>
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<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 5 : break;</span></span>
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<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 58 : selectedFeatures.pop_back();</span></span>
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<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 58 : break;</span></span>
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<span id="L46"><span class="lineNum"> 46</span> : }</span>
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<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 12 : if (selectedFeatures.size() == maxFeatures) {</span></span>
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<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 134 : if (selectedFeatures.size() == maxFeatures) {</span></span>
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<span id="L48"><span class="lineNum"> 48</span> <span class="tlaUNC tlaBgUNC"> 0 : break;</span></span>
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<span id="L49"><span class="lineNum"> 49</span> : }</span>
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<span id="L50"><span class="lineNum"> 50</span> : }</span>
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<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC tlaBgGNC"> 5 : fitted = true;</span></span>
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<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 5 : }</span></span>
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<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC tlaBgGNC"> 58 : fitted = true;</span></span>
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<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 58 : }</span></span>
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<span id="L53"><span class="lineNum"> 53</span> : }</span>
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</pre>
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</td>
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