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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">BoostA2DE.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 <set></span></div>
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="preprocessor">#include <functional></span></div>
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="preprocessor">#include <limits.h></span></div>
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span><span class="preprocessor">#include <tuple></span></div>
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||||
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="preprocessor">#include <folding.hpp></span></div>
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||||
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="preprocessor">#include "bayesnet/feature_selection/CFS.h"</span></div>
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||||
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span><span class="preprocessor">#include "bayesnet/feature_selection/FCBF.h"</span></div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span><span class="preprocessor">#include "bayesnet/feature_selection/IWSS.h"</span></div>
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||||
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span><span class="preprocessor">#include "BoostA2DE.h"</span></div>
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> </div>
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||||
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span><span class="keyword">namespace </span>bayesnet {</div>
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> </div>
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||||
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> BoostA2DE::BoostA2DE(<span class="keywordtype">bool</span> predict_voting) : Boost(predict_voting)</div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> {</div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> }</div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> std::vector<int> BoostA2DE::initializeModels()</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> torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> std::vector<int> featuresSelected = featureSelection(weights_);</div>
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||||
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> <span class="keywordflow">if</span> (featuresSelected.size() < 2) {</div>
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||||
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> notes.push_back(<span class="stringliteral">"No features selected in initialization"</span>);</div>
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||||
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> status = ERROR;</div>
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||||
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> <span class="keywordflow">return</span> std::vector<int>();</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">for</span> (<span class="keywordtype">int</span> i = 0; i < featuresSelected.size() - 1; i++) {</div>
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||||
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = i + 1; j < featuresSelected.size(); j++) {</div>
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||||
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">auto</span> parents = { featuresSelected[i], featuresSelected[j] };</div>
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||||
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);</div>
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||||
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> model->fit(dataset, features, className, states, weights_);</div>
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||||
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> models.push_back(std::move(model));</div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> significanceModels.push_back(1.0); <span class="comment">// They will be updated later in trainModel</span></div>
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||||
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> n_models++;</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> }</div>
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||||
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> notes.push_back(<span class="stringliteral">"Used features in initialization: "</span> + std::to_string(featuresSelected.size()) + <span class="stringliteral">" of "</span> + std::to_string(features.size()) + <span class="stringliteral">" with "</span> + select_features_algorithm);</div>
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||||
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> <span class="keywordflow">return</span> featuresSelected;</div>
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<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> }</div>
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<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> <span class="keywordtype">void</span> BoostA2DE::trainModel(<span class="keyword">const</span> torch::Tensor& weights)</div>
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||||
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> {</div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> <span class="comment">//</span></div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <span class="comment">// Logging setup</span></div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> <span class="comment">//</span></div>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> <span class="comment">// loguru::set_thread_name("BoostA2DE");</span></div>
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||||
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> <span class="comment">// loguru::g_stderr_verbosity = loguru::Verbosity_OFF;</span></div>
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||||
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="comment">// loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);</span></div>
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> </div>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> <span class="comment">// Algorithm based on the adaboost algorithm for classification</span></div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> <span class="comment">// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)</span></div>
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||||
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> fitted = <span class="keyword">true</span>;</div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> <span class="keywordtype">double</span> alpha_t = 0;</div>
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||||
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</div>
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||||
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> <span class="keywordtype">bool</span> finished = <span class="keyword">false</span>;</div>
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||||
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> std::vector<int> featuresUsed;</div>
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<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> <span class="keywordflow">if</span> (selectFeatures) {</div>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> featuresUsed = initializeModels();</div>
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<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> <span class="keyword">auto</span> ypred = predict(X_train);</div>
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<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</div>
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||||
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> <span class="comment">// Update significance of the models</span></div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < n_models; ++i) {</div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> significanceModels[i] = alpha_t;</div>
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||||
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> }</div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> <span class="keywordflow">if</span> (finished) {</div>
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||||
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> <span class="keywordflow">return</span>;</div>
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||||
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> }</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="keywordtype">int</span> numItemsPack = 0; <span class="comment">// The counter of the models inserted in the current pack</span></div>
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||||
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> <span class="comment">// Variables to control the accuracy finish condition</span></div>
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||||
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> <span class="keywordtype">double</span> priorAccuracy = 0.0;</div>
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||||
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> <span class="keywordtype">double</span> improvement = 1.0;</div>
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||||
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> <span class="keywordtype">double</span> convergence_threshold = 1e-4;</div>
|
||||
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> <span class="keywordtype">int</span> tolerance = 0; <span class="comment">// number of times the accuracy is lower than the convergence_threshold</span></div>
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||||
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> <span class="comment">// Step 0: Set the finish condition</span></div>
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||||
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> <span class="comment">// epsilon sub t > 0.5 => inverse the weights policy</span></div>
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||||
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> <span class="comment">// validation error is not decreasing</span></div>
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||||
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="comment">// run out of features</span></div>
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||||
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> <span class="keywordtype">bool</span> ascending = order_algorithm == Orders.ASC;</div>
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<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> std::mt19937 g{ 173 };</div>
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||||
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> std::vector<std::pair<int, int>> pairSelection;</div>
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="keywordflow">while</span> (!finished) {</div>
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||||
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="comment">// Step 1: Build ranking with mutual information</span></div>
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||||
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); <span class="comment">// Get all the pairs sorted</span></div>
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||||
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> <span class="keywordflow">if</span> (order_algorithm == Orders.RAND) {</div>
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<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> std::shuffle(pairSelection.begin(), pairSelection.end(), g);</div>
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||||
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> }</div>
|
||||
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> <span class="keywordtype">int</span> k = bisection ? pow(2, tolerance) : 1;</div>
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||||
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> <span class="keywordtype">int</span> counter = 0; <span class="comment">// The model counter of the current pack</span></div>
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||||
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> <span class="comment">// VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());</span></div>
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||||
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> <span class="keywordflow">while</span> (counter++ < k && pairSelection.size() > 0) {</div>
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||||
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> <span class="keyword">auto</span> feature_pair = pairSelection[0];</div>
|
||||
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> pairSelection.erase(pairSelection.begin());</div>
|
||||
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> std::unique_ptr<Classifier> model;</div>
|
||||
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));</div>
|
||||
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> model->fit(dataset, features, className, states, weights_);</div>
|
||||
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> alpha_t = 0.0;</div>
|
||||
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> <span class="keywordflow">if</span> (!block_update) {</div>
|
||||
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> <span class="keyword">auto</span> ypred = model->predict(X_train);</div>
|
||||
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> <span class="comment">// Step 3.1: Compute the classifier amout of say</span></div>
|
||||
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</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="comment">// Step 3.4: Store classifier and its accuracy to weigh its future vote</span></div>
|
||||
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> numItemsPack++;</div>
|
||||
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> models.push_back(std::move(model));</div>
|
||||
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> significanceModels.push_back(alpha_t);</div>
|
||||
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> n_models++;</div>
|
||||
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> <span class="comment">// VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());</span></div>
|
||||
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> }</div>
|
||||
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> <span class="keywordflow">if</span> (block_update) {</div>
|
||||
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);</div>
|
||||
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> }</div>
|
||||
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> <span class="keywordflow">if</span> (convergence && !finished) {</div>
|
||||
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> <span class="keyword">auto</span> y_val_predict = predict(X_test);</div>
|
||||
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> <span class="keywordtype">double</span> accuracy = (y_val_predict == y_test).sum().item<<span class="keywordtype">double</span>>() / (<span class="keywordtype">double</span>)y_test.size(0);</div>
|
||||
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> <span class="keywordflow">if</span> (priorAccuracy == 0) {</div>
|
||||
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> priorAccuracy = accuracy;</div>
|
||||
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> improvement = accuracy - priorAccuracy;</div>
|
||||
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> }</div>
|
||||
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> <span class="keywordflow">if</span> (improvement < convergence_threshold) {</div>
|
||||
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> <span class="comment">// VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></div>
|
||||
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> tolerance++;</div>
|
||||
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> <span class="comment">// VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></div>
|
||||
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> tolerance = 0; <span class="comment">// Reset the counter if the model performs better</span></div>
|
||||
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> numItemsPack = 0;</div>
|
||||
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> }</div>
|
||||
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> <span class="keywordflow">if</span> (convergence_best) {</div>
|
||||
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> <span class="comment">// Keep the best accuracy until now as the prior accuracy</span></div>
|
||||
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> priorAccuracy = std::max(accuracy, priorAccuracy);</div>
|
||||
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> <span class="comment">// Keep the last accuray obtained as the prior accuracy</span></div>
|
||||
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> priorAccuracy = accuracy;</div>
|
||||
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> }</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> <span class="comment">// VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());</span></div>
|
||||
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;</div>
|
||||
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> }</div>
|
||||
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> <span class="keywordflow">if</span> (tolerance > maxTolerance) {</div>
|
||||
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keywordflow">if</span> (numItemsPack < n_models) {</div>
|
||||
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> notes.push_back(<span class="stringliteral">"Convergence threshold reached & "</span> + std::to_string(numItemsPack) + <span class="stringliteral">" models eliminated"</span>);</div>
|
||||
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> <span class="comment">// VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);</span></div>
|
||||
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < numItemsPack; ++i) {</div>
|
||||
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> significanceModels.pop_back();</div>
|
||||
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> models.pop_back();</div>
|
||||
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> n_models--;</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> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> notes.push_back(<span class="stringliteral">"Convergence threshold reached & 0 models eliminated"</span>);</div>
|
||||
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> <span class="comment">// VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);</span></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> }</div>
|
||||
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> <span class="keywordflow">if</span> (pairSelection.size() > 0) {</div>
|
||||
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> notes.push_back(<span class="stringliteral">"Pairs not used in train: "</span> + std::to_string(pairSelection.size()));</div>
|
||||
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> status = WARNING;</div>
|
||||
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> }</div>
|
||||
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> notes.push_back(<span class="stringliteral">"Number of models: "</span> + std::to_string(n_models));</div>
|
||||
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> }</div>
|
||||
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> std::vector<std::string> BoostA2DE::graph(<span class="keyword">const</span> std::string& title)<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> <span class="keywordflow">return</span> Ensemble::graph(title);</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>}</div>
|
||||
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|
||||
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|
||||
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Reference in New Issue
Block a user