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/ensembles/AODE.cc</title>
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<title>LCOV - BayesNet Coverage Report - bayesnet/ensembles/AODE.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/ensembles</a> - AODE.cc<span style="font-size: 80%;"> (source / <a href="AODE.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/ensembles</a> - AODE.cc<span style="font-size: 80%;"> (source / <a href="AODE.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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@@ -28,7 +28,7 @@
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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">100.0 %</td>
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@@ -37,12 +37,20 @@
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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">4</td>
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<td class="headerCovTableEntry">4</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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@@ -69,33 +77,33 @@
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<span id="L7"><span class="lineNum"> 7</span> : #include "AODE.h"</span>
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<span id="L8"><span class="lineNum"> 8</span> : </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 class="tlaGNC tlaBgGNC"> 38 : AODE::AODE(bool predict_voting) : Ensemble(predict_voting)</span></span>
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<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 76 : AODE::AODE(bool predict_voting) : Ensemble(predict_voting)</span></span>
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<span id="L11"><span class="lineNum"> 11</span> : {</span>
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<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 76 : validHyperparameters = { "predict_voting" };</span></span>
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<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 152 : validHyperparameters = { "predict_voting" };</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"> 114 : }</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 2 : void AODE::setHyperparameters(const nlohmann::json& hyperparameters_)</span></span>
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<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 228 : }</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 4 : void AODE::setHyperparameters(const nlohmann::json& hyperparameters_)</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"> 2 : auto hyperparameters = hyperparameters_;</span></span>
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<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 2 : if (hyperparameters.contains("predict_voting")) {</span></span>
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<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 2 : predict_voting = hyperparameters["predict_voting"];</span></span>
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<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 2 : hyperparameters.erase("predict_voting");</span></span>
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<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 4 : auto hyperparameters = hyperparameters_;</span></span>
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<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 4 : if (hyperparameters.contains("predict_voting")) {</span></span>
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<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 4 : predict_voting = hyperparameters["predict_voting"];</span></span>
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<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 4 : hyperparameters.erase("predict_voting");</span></span>
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<span id="L21"><span class="lineNum"> 21</span> : }</span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 2 : Classifier::setHyperparameters(hyperparameters);</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 2 : }</span></span>
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<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 12 : void AODE::buildModel(const torch::Tensor& weights)</span></span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 4 : Classifier::setHyperparameters(hyperparameters);</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 4 : }</span></span>
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<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 24 : void AODE::buildModel(const torch::Tensor& weights)</span></span>
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<span id="L25"><span class="lineNum"> 25</span> : {</span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 12 : models.clear();</span></span>
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<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 12 : significanceModels.clear();</span></span>
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<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 94 : for (int i = 0; i < features.size(); ++i) {</span></span>
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<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 82 : models.push_back(std::make_unique<SPODE>(i));</span></span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 24 : models.clear();</span></span>
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<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 24 : significanceModels.clear();</span></span>
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<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 188 : for (int i = 0; i < features.size(); ++i) {</span></span>
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<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 164 : models.push_back(std::make_unique<SPODE>(i));</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"> 12 : n_models = models.size();</span></span>
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<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 12 : significanceModels = std::vector<double>(n_models, 1.0);</span></span>
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<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 12 : }</span></span>
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<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 2 : std::vector<std::string> AODE::graph(const std::string& title) const</span></span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 24 : n_models = models.size();</span></span>
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<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 24 : significanceModels = std::vector<double>(n_models, 1.0);</span></span>
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<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 24 : }</span></span>
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<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 4 : std::vector<std::string> AODE::graph(const std::string& title) const</span></span>
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<span id="L35"><span class="lineNum"> 35</span> : {</span>
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<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 2 : return Ensemble::graph(title);</span></span>
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<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 4 : return Ensemble::graph(title);</span></span>
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<span id="L37"><span class="lineNum"> 37</span> : }</span>
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<span id="L38"><span class="lineNum"> 38</span> : }</span>
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</pre>
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