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Enhance tests coverage and report output

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This commit is contained in:
Ricardo Montañana Gómez
2024-04-30 14:00:24 +02:00
parent b4a222b100
commit 3c7382a93a
947 changed files with 376596 additions and 3921 deletions
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html/bayesnet/utils/BayesMetrics.h.gcov.html
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@@ -37,7 +37,7 @@
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-29 20:48:03</td>
<td class="headerValue">2024-04-30 13:59:18</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -74,47 +74,46 @@
<span id="L12"><span class="lineNum"> 12</span> : namespace bayesnet {</span>
<span id="L13"><span class="lineNum"> 13</span> : class Metrics {</span>
<span id="L14"><span class="lineNum"> 14</span> : public:</span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC tlaBgGNC"> 4750 : Metrics() = default;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC tlaBgGNC"> 2658 : Metrics() = default;</span></span>
<span id="L16"><span class="lineNum"> 16</span> : Metrics(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int classNumStates);</span>
<span id="L17"><span class="lineNum"> 17</span> : Metrics(const std::vector&lt;std::vector&lt;int&gt;&gt;&amp; vsamples, const std::vector&lt;int&gt;&amp; labels, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int classNumStates);</span>
<span id="L18"><span class="lineNum"> 18</span> : std::vector&lt;int&gt; SelectKBestWeighted(const torch::Tensor&amp; weights, bool ascending = false, unsigned k = 0);</span>
<span id="L19"><span class="lineNum"> 19</span> : std::vector&lt;double&gt; getScoresKBest() const;</span>
<span id="L20"><span class="lineNum"> 20</span> : double mutualInformation(const torch::Tensor&amp; firstFeature, const torch::Tensor&amp; secondFeature, const torch::Tensor&amp; weights);</span>
<span id="L21"><span class="lineNum"> 21</span> : std::vector&lt;float&gt; conditionalEdgeWeights(std::vector&lt;float&gt;&amp; weights); // To use in Python</span>
<span id="L22"><span class="lineNum"> 22</span> : torch::Tensor conditionalEdge(const torch::Tensor&amp; weights);</span>
<span id="L23"><span class="lineNum"> 23</span> : std::vector&lt;std::pair&lt;int, int&gt;&gt; maximumSpanningTree(const std::vector&lt;std::string&gt;&amp; features, const torch::Tensor&amp; weights, const int root);</span>
<span id="L24"><span class="lineNum"> 24</span> : protected:</span>
<span id="L25"><span class="lineNum"> 25</span> : torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector</span>
<span id="L26"><span class="lineNum"> 26</span> : std::string className;</span>
<span id="L27"><span class="lineNum"> 27</span> : double entropy(const torch::Tensor&amp; feature, const torch::Tensor&amp; weights);</span>
<span id="L28"><span class="lineNum"> 28</span> : std::vector&lt;std::string&gt; features;</span>
<span id="L29"><span class="lineNum"> 29</span> : template &lt;class T&gt;</span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 2225 : std::vector&lt;std::pair&lt;T, T&gt;&gt; doCombinations(const std::vector&lt;T&gt;&amp; source)</span></span>
<span id="L31"><span class="lineNum"> 31</span> : {</span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 2225 : std::vector&lt;std::pair&lt;T, T&gt;&gt; result;</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 11660 : for (int i = 0; i &lt; source.size(); ++i) {</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 9435 : T temp = source[i];</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 29517 : for (int j = i + 1; j &lt; source.size(); ++j) {</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 20082 : result.push_back({ temp, source[j] });</span></span>
<span id="L37"><span class="lineNum"> 37</span> : }</span>
<span id="L38"><span class="lineNum"> 38</span> : }</span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 2225 : return result;</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L41"><span class="lineNum"> 41</span> : template &lt;class T&gt;</span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC tlaBgGNC"> 116 : T pop_first(std::vector&lt;T&gt;&amp; v)</span></span>
<span id="L43"><span class="lineNum"> 43</span> : {</span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 116 : T temp = v[0];</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 116 : v.erase(v.begin());</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 116 : return temp;</span></span>
<span id="L47"><span class="lineNum"> 47</span> : }</span>
<span id="L48"><span class="lineNum"> 48</span> : private:</span>
<span id="L49"><span class="lineNum"> 49</span> : int classNumStates = 0;</span>
<span id="L50"><span class="lineNum"> 50</span> : std::vector&lt;double&gt; scoresKBest;</span>
<span id="L51"><span class="lineNum"> 51</span> : std::vector&lt;int&gt; featuresKBest; // sorted indices of the features</span>
<span id="L52"><span class="lineNum"> 52</span> : double conditionalEntropy(const torch::Tensor&amp; firstFeature, const torch::Tensor&amp; secondFeature, const torch::Tensor&amp; weights);</span>
<span id="L53"><span class="lineNum"> 53</span> : };</span>
<span id="L54"><span class="lineNum"> 54</span> : }</span>
<span id="L55"><span class="lineNum"> 55</span> : #endif</span>
<span id="L21"><span class="lineNum"> 21</span> : torch::Tensor conditionalEdge(const torch::Tensor&amp; weights);</span>
<span id="L22"><span class="lineNum"> 22</span> : std::vector&lt;std::pair&lt;int, int&gt;&gt; maximumSpanningTree(const std::vector&lt;std::string&gt;&amp; features, const torch::Tensor&amp; weights, const int root);</span>
<span id="L23"><span class="lineNum"> 23</span> : protected:</span>
<span id="L24"><span class="lineNum"> 24</span> : torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector</span>
<span id="L25"><span class="lineNum"> 25</span> : std::string className;</span>
<span id="L26"><span class="lineNum"> 26</span> : double entropy(const torch::Tensor&amp; feature, const torch::Tensor&amp; weights);</span>
<span id="L27"><span class="lineNum"> 27</span> : std::vector&lt;std::string&gt; features;</span>
<span id="L28"><span class="lineNum"> 28</span> : template &lt;class T&gt;</span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 1251 : std::vector&lt;std::pair&lt;T, T&gt;&gt; doCombinations(const std::vector&lt;T&gt;&amp; source)</span></span>
<span id="L30"><span class="lineNum"> 30</span> : {</span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 1251 : std::vector&lt;std::pair&lt;T, T&gt;&gt; result;</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 6532 : for (int i = 0; i &lt; source.size(); ++i) {</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 5281 : T temp = source[i];</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 16445 : for (int j = i + 1; j &lt; source.size(); ++j) {</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 11164 : result.push_back({ temp, source[j] });</span></span>
<span id="L36"><span class="lineNum"> 36</span> : }</span>
<span id="L37"><span class="lineNum"> 37</span> : }</span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 1251 : return result;</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L40"><span class="lineNum"> 40</span> : template &lt;class T&gt;</span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC tlaBgGNC"> 68 : T pop_first(std::vector&lt;T&gt;&amp; v)</span></span>
<span id="L42"><span class="lineNum"> 42</span> : {</span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 68 : T temp = v[0];</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 68 : v.erase(v.begin());</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 68 : return temp;</span></span>
<span id="L46"><span class="lineNum"> 46</span> : }</span>
<span id="L47"><span class="lineNum"> 47</span> : private:</span>
<span id="L48"><span class="lineNum"> 48</span> : int classNumStates = 0;</span>
<span id="L49"><span class="lineNum"> 49</span> : std::vector&lt;double&gt; scoresKBest;</span>
<span id="L50"><span class="lineNum"> 50</span> : std::vector&lt;int&gt; featuresKBest; // sorted indices of the features</span>
<span id="L51"><span class="lineNum"> 51</span> : double conditionalEntropy(const torch::Tensor&amp; firstFeature, const torch::Tensor&amp; secondFeature, const torch::Tensor&amp; weights);</span>
<span id="L52"><span class="lineNum"> 52</span> : };</span>
<span id="L53"><span class="lineNum"> 53</span> : }</span>
<span id="L54"><span class="lineNum"> 54</span> : #endif</span>
</pre>
</td>
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