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/classifiers/KDBLd.cc</title>
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<title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/KDBLd.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/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (source / <a href="KDBLd.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/classifiers</a> - KDBLd.cc<span style="font-size: 80%;"> (source / <a href="KDBLd.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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@@ -69,30 +77,30 @@
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<span id="L7"><span class="lineNum"> 7</span> : #include "KDBLd.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"> 34 : KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}</span></span>
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<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 10 : KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
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<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 68 : KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}</span></span>
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<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 20 : KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
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<span id="L12"><span class="lineNum"> 12</span> : {</span>
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<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 10 : checkInput(X_, y_);</span></span>
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<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 10 : features = features_;</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 10 : className = className_;</span></span>
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<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 10 : Xf = X_;</span></span>
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<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 10 : y = y_;</span></span>
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<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 20 : checkInput(X_, y_);</span></span>
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<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 20 : features = features_;</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 20 : className = className_;</span></span>
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<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 20 : Xf = X_;</span></span>
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<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 20 : y = y_;</span></span>
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<span id="L18"><span class="lineNum"> 18</span> : // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y</span>
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<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 10 : states = fit_local_discretization(y);</span></span>
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<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 20 : states = fit_local_discretization(y);</span></span>
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<span id="L20"><span class="lineNum"> 20</span> : // We have discretized the input data</span>
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<span id="L21"><span class="lineNum"> 21</span> : // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network</span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 10 : KDB::fit(dataset, features, className, states);</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 10 : states = localDiscretizationProposal(states, model);</span></span>
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<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 10 : return *this;</span></span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 20 : KDB::fit(dataset, features, className, states);</span></span>
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<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 20 : states = localDiscretizationProposal(states, model);</span></span>
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<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 20 : return *this;</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"> 8 : torch::Tensor KDBLd::predict(torch::Tensor& X)</span></span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 16 : torch::Tensor KDBLd::predict(torch::Tensor& X)</span></span>
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<span id="L27"><span class="lineNum"> 27</span> : {</span>
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<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 8 : auto Xt = prepareX(X);</span></span>
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<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 16 : return KDB::predict(Xt);</span></span>
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<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 8 : }</span></span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 2 : std::vector<std::string> KDBLd::graph(const std::string& name) const</span></span>
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<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 16 : auto Xt = prepareX(X);</span></span>
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<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 32 : return KDB::predict(Xt);</span></span>
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<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 16 : }</span></span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 4 : std::vector<std::string> KDBLd::graph(const std::string& name) const</span></span>
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<span id="L32"><span class="lineNum"> 32</span> : {</span>
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<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 2 : return KDB::graph(name);</span></span>
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<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 4 : return KDB::graph(name);</span></span>
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<span id="L34"><span class="lineNum"> 34</span> : }</span>
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<span id="L35"><span class="lineNum"> 35</span> : }</span>
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</pre>
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