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<div class="headertitle"><div class="title">Proposal.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 "Proposal.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> Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}</div>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> Proposal::~Proposal()</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> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& [key, value] : discretizers) {</div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> <span class="keyword">delete</span> value;</div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</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="keywordtype">void</span> Proposal::checkInput(<span class="keyword">const</span> torch::Tensor& X, <span class="keyword">const</span> torch::Tensor& y)</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> <span class="keywordflow">if</span> (!torch::is_floating_point(X)) {</div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"X must be a floating point tensor"</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> <span class="keywordflow">if</span> (torch::is_floating_point(y)) {</div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"y must be an integer tensor"</span>);</div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> }</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> map<std::string, std::vector<int>> Proposal::localDiscretizationProposal(<span class="keyword">const</span> map<std::string, std::vector<int>>& oldStates, Network& model)</div>
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<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> {</div>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> <span class="comment">// order of local discretization is important. no good 0, 1, 2...</span></div>
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> <span class="comment">// although we rediscretize features after the local discretization of every feature</span></div>
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">auto</span> order = model.topological_sort();</div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">auto</span>& nodes = model.getNodes();</div>
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> map<std::string, std::vector<int>> states = oldStates;</div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> std::vector<int> indicesToReDiscretize;</div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> <span class="keywordtype">bool</span> upgrade = <span class="keyword">false</span>; <span class="comment">// Flag to check if we need to upgrade the model</span></div>
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<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> feature : order) {</div>
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<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">auto</span> nodeParents = nodes[feature]->getParents();</div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> <span class="keywordflow">if</span> (nodeParents.size() < 2) <span class="keywordflow">continue</span>; <span class="comment">// Only has class as parent</span></div>
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<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> upgrade = <span class="keyword">true</span>;</div>
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<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> <span class="keywordtype">int</span> index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();</div>
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<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> indicesToReDiscretize.push_back(index); <span class="comment">// We need to re-discretize this feature</span></div>
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<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> std::vector<std::string> parents;</div>
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<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](<span class="keyword">const</span> <span class="keyword">auto</span>& p) { <span class="keywordflow">return</span> p->getName(); });</div>
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<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> <span class="comment">// Remove class as parent as it will be added later</span></div>
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<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());</div>
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<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> <span class="comment">// Get the indices of the parents</span></div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> std::vector<int> indices;</div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> indices.push_back(-1); <span class="comment">// Add class index</span></div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> transform(parents.begin(), parents.end(), back_inserter(indices), [&](<span class="keyword">const</span> <span class="keyword">auto</span>& p) {<span class="keywordflow">return</span> find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });</div>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> <span class="comment">// Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)</span></div>
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<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> std::vector<std::string> yJoinParents(Xf.size(1));</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="keyword">auto</span> idx : indices) {</div>
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < Xf.size(1); ++i) {</div>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<<span class="keywordtype">int</span>>());</div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> }</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="keyword">auto</span> yxv = factorize(yJoinParents);</div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> <span class="keyword">auto</span> xvf_ptr = Xf.index({ index }).data_ptr<float>();</div>
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> <span class="keyword">auto</span> xvf = std::vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));</div>
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<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> discretizers[feature]->fit(xvf, yxv);</div>
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<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> }</div>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> <span class="keywordflow">if</span> (upgrade) {</div>
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<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> <span class="comment">// Discretize again X (only the affected indices) with the new fitted discretizers</span></div>
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<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> index : indicesToReDiscretize) {</div>
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<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> <span class="keyword">auto</span> Xt_ptr = Xf.index({ index }).data_ptr<float>();</div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="keyword">auto</span> Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));</div>
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> pDataset.index_put_({ index, <span class="stringliteral">"..."</span> }, torch::tensor(discretizers[pFeatures[index]]->transform(Xt)));</div>
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<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> <span class="keyword">auto</span> xStates = std::vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);</div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> iota(xStates.begin(), xStates.end(), 0);</div>
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<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> <span class="comment">//Update new states of the feature/node</span></div>
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<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> states[pFeatures[index]] = xStates;</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="keyword">const</span> torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);</div>
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> model.fit(pDataset, weights, pFeatures, pClassName, states);</div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> }</div>
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<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> <span class="keywordflow">return</span> states;</div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> }</div>
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<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> map<std::string, std::vector<int>> Proposal::fit_local_discretization(<span class="keyword">const</span> torch::Tensor& y)</div>
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<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> {</div>
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> <span class="comment">// Discretize the continuous input data and build pDataset (Classifier::dataset)</span></div>
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<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> <span class="keywordtype">int</span> m = Xf.size(1);</div>
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<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="keywordtype">int</span> n = Xf.size(0);</div>
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<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> map<std::string, std::vector<int>> states;</div>
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<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> pDataset = torch::zeros({ n + 1, m }, torch::kInt32);</div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> <span class="keyword">auto</span> yv = std::vector<int>(y.data_ptr<<span class="keywordtype">int</span>>(), y.data_ptr<<span class="keywordtype">int</span>>() + y.size(0));</div>
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="comment">// discretize input data by feature(row)</span></div>
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < pFeatures.size(); ++i) {</div>
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> <span class="keyword">auto</span>* discretizer = <span class="keyword">new</span> mdlp::CPPFImdlp();</div>
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<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> <span class="keyword">auto</span> Xt_ptr = Xf.index({ i }).data_ptr<float>();</div>
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<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> <span class="keyword">auto</span> Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));</div>
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<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> discretizer->fit(Xt, yv);</div>
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<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> pDataset.index_put_({ i, <span class="stringliteral">"..."</span> }, torch::tensor(discretizer->transform(Xt)));</div>
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<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> <span class="keyword">auto</span> xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);</div>
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<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> iota(xStates.begin(), xStates.end(), 0);</div>
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<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> states[pFeatures[i]] = xStates;</div>
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<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> discretizers[pFeatures[i]] = discretizer;</div>
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<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> }</div>
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<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> <span class="keywordtype">int</span> n_classes = torch::max(y).item<<span class="keywordtype">int</span>>() + 1;</div>
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<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> <span class="keyword">auto</span> yStates = std::vector<int>(n_classes);</div>
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<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> iota(yStates.begin(), yStates.end(), 0);</div>
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<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> states[pClassName] = yStates;</div>
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<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> pDataset.index_put_({ n, <span class="stringliteral">"..."</span> }, y);</div>
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<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> <span class="keywordflow">return</span> states;</div>
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<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> }</div>
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<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> torch::Tensor Proposal::prepareX(torch::Tensor& X)</div>
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<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> {</div>
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<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> <span class="keyword">auto</span> Xtd = torch::zeros_like(X, torch::kInt32);</div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < X.size(0); ++i) {</div>
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<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> <span class="keyword">auto</span> Xt = std::vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));</div>
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<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> <span class="keyword">auto</span> Xd = discretizers[pFeatures[i]]->transform(Xt);</div>
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<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));</div>
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> }</div>
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<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> <span class="keywordflow">return</span> Xtd;</div>
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<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> }</div>
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<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> std::vector<int> Proposal::factorize(<span class="keyword">const</span> std::vector<std::string>& labels_t)</div>
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<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> {</div>
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<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> std::vector<int> yy;</div>
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> yy.reserve(labels_t.size());</div>
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<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> std::map<std::string, int> labelMap;</div>
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<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> <span class="keywordtype">int</span> i = 0;</div>
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<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& label : labels_t) {</div>
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<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> <span class="keywordflow">if</span> (labelMap.find(label) == labelMap.end()) {</div>
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<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> labelMap[label] = i++;</div>
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<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> <span class="keywordtype">bool</span> allDigits = std::all_of(label.begin(), label.end(), ::isdigit);</div>
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<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> }</div>
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<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> yy.push_back(labelMap[label]);</div>
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<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> }</div>
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<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keywordflow">return</span> yy;</div>
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<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> }</div>
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<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span>}</div>
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