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<div id="projectname">BayesNet<span id="projectnumber"> 1.0.5</span>
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<div id="projectbrief">Bayesian Network Classifiers using libtorch from scratch</div>
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<div class="headertitle"><div class="title">Ensemble.cc</div></div>
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="comment">// ***************************************************************</span></div>
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<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="comment">// ***************************************************************</span></div>
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<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span> </div>
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span><span class="preprocessor">#include "Ensemble.h"</span></div>
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span> </div>
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="keyword">namespace </span>bayesnet {</div>
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span> </div>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> Ensemble::Ensemble(<span class="keywordtype">bool</span> predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)</div>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> {</div>
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||||
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> </div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> };</div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">const</span> std::string ENSEMBLE_NOT_FITTED = <span class="stringliteral">"Ensemble has not been fitted"</span>;</div>
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> <span class="keywordtype">void</span> Ensemble::trainModel(<span class="keyword">const</span> torch::Tensor& weights)</div>
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> {</div>
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||||
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> n_models = models.size();</div>
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||||
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> <span class="comment">// fit with std::vectors</span></div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> models[i]->fit(dataset, features, className, states);</div>
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||||
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> }</div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> }</div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)</div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> {</div>
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<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> std::vector<int> y_pred;</div>
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||||
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < X.size(); ++i) {</div>
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||||
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> <span class="keyword">auto</span> max = std::max_element(X[i].begin(), X[i].end());</div>
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||||
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> y_pred.push_back(std::distance(X[i].begin(), max));</div>
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||||
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> }</div>
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||||
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> <span class="keywordflow">return</span> y_pred;</div>
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> }</div>
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||||
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> torch::Tensor Ensemble::compute_arg_max(torch::Tensor& X)</div>
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||||
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> {</div>
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||||
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">auto</span> y_pred = torch::argmax(X, 1);</div>
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||||
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> <span class="keywordflow">return</span> y_pred;</div>
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||||
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> }</div>
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||||
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> torch::Tensor Ensemble::voting(torch::Tensor& votes)</div>
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||||
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> {</div>
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||||
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> <span class="comment">// Convert m x n_models tensor to a m x n_class_states with voting probabilities</span></div>
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||||
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">auto</span> y_pred_ = votes.accessor<int, 2>();</div>
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||||
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> std::vector<int> y_pred_final;</div>
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||||
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> <span class="keywordtype">int</span> numClasses = states.at(className).size();</div>
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||||
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> <span class="comment">// votes is m x n_models with the prediction of every model for each sample</span></div>
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<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">auto</span> result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);</div>
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||||
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">auto</span> sum = std::reduce(significanceModels.begin(), significanceModels.end());</div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < votes.size(0); ++i) {</div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> <span class="comment">// n_votes store in each index (value of class) the significance added by each model</span></div>
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||||
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> <span class="comment">// i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions</span></div>
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<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> std::vector<double> n_votes(numClasses, 0.0);</div>
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||||
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < n_models; ++j) {</div>
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> n_votes[y_pred_[i][j]] += significanceModels.at(j);</div>
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||||
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> }</div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> result[i] = torch::tensor(n_votes);</div>
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<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> }</div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> <span class="comment">// To only do one division and gain precision</span></div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> result /= sum;</div>
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||||
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> <span class="keywordflow">return</span> result;</div>
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<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> }</div>
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<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> std::vector<std::vector<double>> Ensemble::predict_proba(std::vector<std::vector<int>>& X)</div>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> {</div>
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<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> <span class="keywordflow">if</span> (!fitted) {</div>
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<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> <span class="keywordflow">throw</span> std::logic_error(ENSEMBLE_NOT_FITTED);</div>
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||||
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> }</div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="keywordflow">return</span> predict_voting ? predict_average_voting(X) : predict_average_proba(X);</div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> }</div>
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||||
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> torch::Tensor Ensemble::predict_proba(torch::Tensor& X)</div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> {</div>
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||||
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> <span class="keywordflow">if</span> (!fitted) {</div>
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||||
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> <span class="keywordflow">throw</span> std::logic_error(ENSEMBLE_NOT_FITTED);</div>
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||||
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> }</div>
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> <span class="keywordflow">return</span> predict_voting ? predict_average_voting(X) : predict_average_proba(X);</div>
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||||
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> }</div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> std::vector<int> Ensemble::predict(std::vector<std::vector<int>>& X)</div>
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||||
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> {</div>
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||||
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> <span class="keyword">auto</span> res = predict_proba(X);</div>
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||||
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> <span class="keywordflow">return</span> compute_arg_max(res);</div>
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||||
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> }</div>
|
||||
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> torch::Tensor Ensemble::predict(torch::Tensor& X)</div>
|
||||
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> {</div>
|
||||
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="keyword">auto</span> res = predict_proba(X);</div>
|
||||
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> <span class="keywordflow">return</span> compute_arg_max(res);</div>
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||||
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> }</div>
|
||||
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> torch::Tensor Ensemble::predict_average_proba(torch::Tensor& X)</div>
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||||
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> {</div>
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="keyword">auto</span> n_states = models[0]->getClassNumStates();</div>
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||||
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);</div>
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||||
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> <span class="keyword">auto</span> threads{ std::vector<std::thread>() };</div>
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||||
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> std::mutex mtx;</div>
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<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
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||||
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> threads.push_back(std::thread([&, i]() {</div>
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||||
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> <span class="keyword">auto</span> ypredict = models[i]->predict_proba(X);</div>
|
||||
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> std::lock_guard<std::mutex> lock(mtx);</div>
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||||
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> y_pred += ypredict * significanceModels[i];</div>
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||||
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> }));</div>
|
||||
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> }</div>
|
||||
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& thread : threads) {</div>
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||||
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> thread.join();</div>
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||||
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> }</div>
|
||||
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> <span class="keyword">auto</span> sum = std::reduce(significanceModels.begin(), significanceModels.end());</div>
|
||||
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> y_pred /= sum;</div>
|
||||
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> <span class="keywordflow">return</span> y_pred;</div>
|
||||
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> }</div>
|
||||
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> std::vector<std::vector<double>> Ensemble::predict_average_proba(std::vector<std::vector<int>>& X)</div>
|
||||
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> {</div>
|
||||
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> <span class="keyword">auto</span> n_states = models[0]->getClassNumStates();</div>
|
||||
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));</div>
|
||||
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> <span class="keyword">auto</span> threads{ std::vector<std::thread>() };</div>
|
||||
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> std::mutex mtx;</div>
|
||||
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
|
||||
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> threads.push_back(std::thread([&, i]() {</div>
|
||||
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> <span class="keyword">auto</span> ypredict = models[i]->predict_proba(X);</div>
|
||||
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> assert(ypredict.size() == y_pred.size());</div>
|
||||
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> assert(ypredict[0].size() == y_pred[0].size());</div>
|
||||
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> std::lock_guard<std::mutex> lock(mtx);</div>
|
||||
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> <span class="comment">// Multiply each prediction by the significance of the model and then add it to the final prediction</span></div>
|
||||
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j < ypredict.size(); ++j) {</div>
|
||||
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),</div>
|
||||
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> [significanceModels = significanceModels[i]](<span class="keywordtype">double</span> x, <span class="keywordtype">double</span> y) { <span class="keywordflow">return</span> x + y * significanceModels; });</div>
|
||||
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> }</div>
|
||||
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> }));</div>
|
||||
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> }</div>
|
||||
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& thread : threads) {</div>
|
||||
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> thread.join();</div>
|
||||
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> }</div>
|
||||
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keyword">auto</span> sum = std::reduce(significanceModels.begin(), significanceModels.end());</div>
|
||||
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="comment">//Divide each element of the prediction by the sum of the significances</span></div>
|
||||
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j < y_pred.size(); ++j) {</div>
|
||||
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](<span class="keywordtype">double</span> x) { <span class="keywordflow">return</span> x / sum; });</div>
|
||||
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> }</div>
|
||||
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <span class="keywordflow">return</span> y_pred;</div>
|
||||
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> }</div>
|
||||
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> std::vector<std::vector<double>> Ensemble::predict_average_voting(std::vector<std::vector<int>>& X)</div>
|
||||
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> {</div>
|
||||
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> torch::Tensor Xt = bayesnet::vectorToTensor(X, <span class="keyword">false</span>);</div>
|
||||
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> <span class="keyword">auto</span> y_pred = predict_average_voting(Xt);</div>
|
||||
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> std::vector<std::vector<double>> result = tensorToVectorDouble(y_pred);</div>
|
||||
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> <span class="keywordflow">return</span> result;</div>
|
||||
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> }</div>
|
||||
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> torch::Tensor Ensemble::predict_average_voting(torch::Tensor& X)</div>
|
||||
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> {</div>
|
||||
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> <span class="comment">// Build a m x n_models tensor with the predictions of each model</span></div>
|
||||
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);</div>
|
||||
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keyword">auto</span> threads{ std::vector<std::thread>() };</div>
|
||||
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> std::mutex mtx;</div>
|
||||
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
|
||||
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> threads.push_back(std::thread([&, i]() {</div>
|
||||
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> <span class="keyword">auto</span> ypredict = models[i]->predict(X);</div>
|
||||
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> std::lock_guard<std::mutex> lock(mtx);</div>
|
||||
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> y_pred.index_put_({ <span class="stringliteral">"..."</span>, i }, ypredict);</div>
|
||||
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> }));</div>
|
||||
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> }</div>
|
||||
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& thread : threads) {</div>
|
||||
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> thread.join();</div>
|
||||
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> }</div>
|
||||
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> <span class="keywordflow">return</span> voting(y_pred);</div>
|
||||
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> }</div>
|
||||
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> <span class="keywordtype">float</span> Ensemble::score(torch::Tensor& X, torch::Tensor& y)</div>
|
||||
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> {</div>
|
||||
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> <span class="keyword">auto</span> y_pred = predict(X);</div>
|
||||
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> <span class="keywordtype">int</span> correct = 0;</div>
|
||||
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < y_pred.size(0); ++i) {</div>
|
||||
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> <span class="keywordflow">if</span> (y_pred[i].item<int>() == y[i].item<int>()) {</div>
|
||||
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> correct++;</div>
|
||||
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> }</div>
|
||||
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> }</div>
|
||||
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> <span class="keywordflow">return</span> (<span class="keywordtype">double</span>)correct / y_pred.size(0);</div>
|
||||
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> }</div>
|
||||
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> <span class="keywordtype">float</span> Ensemble::score(std::vector<std::vector<int>>& X, std::vector<int>& y)</div>
|
||||
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> {</div>
|
||||
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> <span class="keyword">auto</span> y_pred = predict(X);</div>
|
||||
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> <span class="keywordtype">int</span> correct = 0;</div>
|
||||
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < y_pred.size(); ++i) {</div>
|
||||
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> <span class="keywordflow">if</span> (y_pred[i] == y[i]) {</div>
|
||||
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> correct++;</div>
|
||||
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> }</div>
|
||||
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> }</div>
|
||||
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> <span class="keywordflow">return</span> (<span class="keywordtype">double</span>)correct / y_pred.size();</div>
|
||||
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> }</div>
|
||||
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> std::vector<std::string> Ensemble::show()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> <span class="keyword">auto</span> result = std::vector<std::string>();</div>
|
||||
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
|
||||
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> <span class="keyword">auto</span> res = models[i]->show();</div>
|
||||
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> result.insert(result.end(), res.begin(), res.end());</div>
|
||||
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> }</div>
|
||||
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> <span class="keywordflow">return</span> result;</div>
|
||||
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> }</div>
|
||||
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> std::vector<std::string> Ensemble::graph(<span class="keyword">const</span> std::string& title)<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> <span class="keyword">auto</span> result = std::vector<std::string>();</div>
|
||||
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
|
||||
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> <span class="keyword">auto</span> res = models[i]->graph(title + <span class="stringliteral">"_"</span> + std::to_string(i));</div>
|
||||
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> result.insert(result.end(), res.begin(), res.end());</div>
|
||||
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> }</div>
|
||||
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> <span class="keywordflow">return</span> result;</div>
|
||||
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> }</div>
|
||||
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> <span class="keywordtype">int</span> Ensemble::getNumberOfNodes()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> <span class="keywordtype">int</span> nodes = 0;</div>
|
||||
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
|
||||
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> nodes += models[i]->getNumberOfNodes();</div>
|
||||
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> }</div>
|
||||
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> <span class="keywordflow">return</span> nodes;</div>
|
||||
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> }</div>
|
||||
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> <span class="keywordtype">int</span> Ensemble::getNumberOfEdges()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> <span class="keywordtype">int</span> edges = 0;</div>
|
||||
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
|
||||
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> edges += models[i]->getNumberOfEdges();</div>
|
||||
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> }</div>
|
||||
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> <span class="keywordflow">return</span> edges;</div>
|
||||
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> }</div>
|
||||
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> <span class="keywordtype">int</span> Ensemble::getNumberOfStates()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> <span class="keywordtype">int</span> nstates = 0;</div>
|
||||
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_models; ++i) {</div>
|
||||
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span> nstates += models[i]->getNumberOfStates();</div>
|
||||
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> }</div>
|
||||
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> <span class="keywordflow">return</span> nstates;</div>
|
||||
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> }</div>
|
||||
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span>}</div>
|
||||
</div><!-- fragment --></div><!-- contents -->
|
||||
</div><!-- doc-content -->
|
||||
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|
||||
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|
||||
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|
||||
<li class="navelem"><a class="el" href="dir_40070fdff85d618b4d1d3ab4ac4f79bb.html">bayesnet</a></li><li class="navelem"><a class="el" href="dir_2f68445c4ac4316280c650d0a13b2741.html">ensembles</a></li><li class="navelem"><b>Ensemble.cc</b></li>
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|
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Reference in New Issue
Block a user