Refactor coverage report generation
Add some tests to reach 99%
This commit is contained in:
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<head>
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<title>LCOV - coverage.info - bayesnet/feature_selection/FCBF.cc</title>
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<title>LCOV - BayesNet Coverage Report - bayesnet/feature_selection/FCBF.cc</title>
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<link rel="stylesheet" type="text/css" href="../../gcov.css">
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</head>
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@@ -19,7 +19,7 @@
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<table cellpadding=1 border=0 width="100%">
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<tr>
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<td width="10%" class="headerItem">Current view:</td>
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<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/feature_selection</a> - FCBF.cc<span style="font-size: 80%;"> (source / <a href="FCBF.cc.func-c.html">functions</a>)</span></td>
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<td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/feature_selection</a> - FCBF.cc<span style="font-size: 80%;"> (source / <a href="FCBF.cc.func-c.html">functions</a>)</span></td>
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<td width="5%"></td>
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<td width="5%"></td>
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<td width="5%" class="headerCovTableHead">Coverage</td>
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@@ -28,21 +28,29 @@
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</tr>
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<tr>
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<td class="headerItem">Test:</td>
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<td class="headerValue">coverage.info</td>
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<td class="headerValue">BayesNet Coverage Report</td>
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<td></td>
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<td class="headerItem">Lines:</td>
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<td class="headerCovTableEntryHi">92.3 %</td>
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<td class="headerCovTableEntryHi">100.0 %</td>
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<td class="headerCovTableEntry">26</td>
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<td class="headerCovTableEntry">26</td>
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<td class="headerCovTableEntry">24</td>
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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">2024-04-30 20:26:57</td>
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<td class="headerValue">2024-05-06 17:54:04</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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<td class="headerCovTableEntry">2</td>
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<td class="headerCovTableEntry">2</td>
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</tr>
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<tr>
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<td class="headerItem">Legend:</td>
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<td class="headerValueLeg"> Lines:
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<span class="coverLegendCov">hit</span>
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<span class="coverLegendNoCov">not hit</span>
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</td>
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<td></td>
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</tr>
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<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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</table>
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@@ -70,45 +78,45 @@
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<span id="L8"><span class="lineNum"> 8</span> : #include "FCBF.h"</span>
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<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
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<span id="L10"><span class="lineNum"> 10</span> : </span>
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<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 14 : FCBF::FCBF(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"> 14 : 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"> 76 : FCBF::FCBF(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"> 76 : 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"> 14 : if (threshold < 1e-7) {</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 4 : throw std::invalid_argument("Threshold cannot be less than 1e-7");</span></span>
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<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 76 : if (threshold < 1e-7) {</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 20 : throw std::invalid_argument("Threshold cannot be less than 1e-7");</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"> 14 : }</span></span>
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<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 10 : void FCBF::fit()</span></span>
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<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 76 : }</span></span>
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<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 56 : void FCBF::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"> 10 : initialize();</span></span>
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<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 10 : computeSuLabels();</span></span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 10 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 10 : auto featureOrderCopy = featureOrder;</span></span>
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<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 84 : for (const auto& feature : featureOrder) {</span></span>
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<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 56 : initialize();</span></span>
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<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 56 : computeSuLabels();</span></span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 56 : auto featureOrder = argsort(suLabels); // sort descending order</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 56 : auto featureOrderCopy = featureOrder;</span></span>
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<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 419 : for (const auto& feature : featureOrder) {</span></span>
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<span id="L25"><span class="lineNum"> 25</span> : // Don't self compare</span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 74 : featureOrderCopy.erase(featureOrderCopy.begin());</span></span>
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<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 74 : if (suLabels.at(feature) == 0.0) {</span></span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 372 : featureOrderCopy.erase(featureOrderCopy.begin());</span></span>
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<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 372 : if (suLabels.at(feature) == 0.0) {</span></span>
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<span id="L28"><span class="lineNum"> 28</span> : // The feature has been removed from the list</span>
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<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 32 : continue;</span></span>
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<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 153 : continue;</span></span>
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<span id="L30"><span class="lineNum"> 30</span> : }</span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 42 : if (suLabels.at(feature) < threshold) {</span></span>
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<span id="L32"><span class="lineNum"> 32</span> <span class="tlaUNC tlaBgUNC"> 0 : break;</span></span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 219 : if (suLabels.at(feature) < threshold) {</span></span>
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<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 5 : break;</span></span>
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<span id="L33"><span class="lineNum"> 33</span> : }</span>
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<span id="L34"><span class="lineNum"> 34</span> : // Remove redundant features</span>
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<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC tlaBgGNC"> 232 : for (const auto& featureCopy : featureOrderCopy) {</span></span>
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<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 190 : double value = computeSuFeatures(feature, featureCopy);</span></span>
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<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 190 : if (value >= suLabels.at(featureCopy)) {</span></span>
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<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 1220 : for (const auto& featureCopy : featureOrderCopy) {</span></span>
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<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 1006 : double value = computeSuFeatures(feature, featureCopy);</span></span>
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<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 1006 : if (value >= suLabels.at(featureCopy)) {</span></span>
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<span id="L38"><span class="lineNum"> 38</span> : // Remove feature from list</span>
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<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 66 : suLabels[featureCopy] = 0.0;</span></span>
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<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 333 : suLabels[featureCopy] = 0.0;</span></span>
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<span id="L40"><span class="lineNum"> 40</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"> 42 : selectedFeatures.push_back(feature);</span></span>
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<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 42 : selectedScores.push_back(suLabels[feature]);</span></span>
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<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 42 : if (selectedFeatures.size() == maxFeatures) {</span></span>
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<span id="L45"><span class="lineNum"> 45</span> <span class="tlaUNC tlaBgUNC"> 0 : break;</span></span>
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<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 214 : selectedFeatures.push_back(feature);</span></span>
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<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 214 : selectedScores.push_back(suLabels[feature]);</span></span>
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<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 214 : if (selectedFeatures.size() == maxFeatures) {</span></span>
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<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 4 : 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>
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<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC tlaBgGNC"> 10 : fitted = true;</span></span>
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<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 10 : }</span></span>
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<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 56 : fitted = true;</span></span>
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<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 56 : }</span></span>
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<span id="L50"><span class="lineNum"> 50</span> : }</span>
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</pre>
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</td>
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