26 KiB
26 KiB
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Line data Source code 1 : // *************************************************************** 2 : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez 3 : // SPDX-FileType: SOURCE 4 : // SPDX-License-Identifier: MIT 5 : // *************************************************************** 6 : 7 : #include "Mst.h" 8 : #include "BayesMetrics.h" 9 : namespace bayesnet { 10 : //samples is n+1xm tensor used to fit the model 11 2248 : Metrics::Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates) 12 2248 : : samples(samples) 13 2248 : , className(className) 14 2248 : , features(features) 15 2248 : , classNumStates(classNumStates) 16 : { 17 2248 : } 18 : //samples is n+1xm std::vector used to fit the model 19 96 : Metrics::Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates) 20 96 : : samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32)) 21 96 : , className(className) 22 96 : , features(features) 23 96 : , classNumStates(classNumStates) 24 : { 25 768 : for (int i = 0; i < vsamples.size(); ++i) { 26 2688 : samples.index_put_({ i, "..." }, torch::tensor(vsamples[i], torch::kInt32)); 27 : } 28 384 : samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32)); 29 864 : } 30 690 : std::vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k) 31 : { 32 : // Return the K Best features 33 690 : auto n = features.size(); 34 690 : if (k == 0) { 35 0 : k = n; 36 : } 37 : // compute scores 38 690 : scoresKBest.clear(); 39 690 : featuresKBest.clear(); 40 2070 : auto label = samples.index({ -1, "..." }); 41 15576 : for (int i = 0; i < n; ++i) { 42 44658 : scoresKBest.push_back(mutualInformation(label, samples.index({ i, "..." }), weights)); 43 14886 : featuresKBest.push_back(i); 44 : } 45 : // sort & reduce scores and features 46 690 : if (ascending) { 47 114 : sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j) 48 2718 : { return scoresKBest[i] < scoresKBest[j]; }); 49 114 : sort(scoresKBest.begin(), scoresKBest.end(), std::less<double>()); 50 114 : if (k < n) { 51 168 : for (int i = 0; i < n - k; ++i) { 52 120 : featuresKBest.erase(featuresKBest.begin()); 53 120 : scoresKBest.erase(scoresKBest.begin()); 54 : } 55 : } 56 : } else { 57 576 : sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j) 58 97212 : { return scoresKBest[i] > scoresKBest[j]; }); 59 576 : sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>()); 60 576 : featuresKBest.resize(k); 61 576 : scoresKBest.resize(k); 62 : } 63 1380 : return featuresKBest; 64 16266 : } 65 48 : std::vector<double> Metrics::getScoresKBest() const 66 : { 67 48 : return scoresKBest; 68 : } 69 : 70 204 : torch::Tensor Metrics::conditionalEdge(const torch::Tensor& weights) 71 : { 72 204 : auto result = std::vector<double>(); 73 204 : auto source = std::vector<std::string>(features); 74 204 : source.push_back(className); 75 204 : auto combinations = doCombinations(source); 76 : // Compute class prior 77 204 : auto margin = torch::zeros({ classNumStates }, torch::kFloat); 78 1104 : for (int value = 0; value < classNumStates; ++value) { 79 3600 : auto mask = samples.index({ -1, "..." }) == value; 80 900 : margin[value] = mask.sum().item<double>() / samples.size(1); 81 900 : } 82 5508 : for (auto [first, second] : combinations) { 83 5304 : int index_first = find(features.begin(), features.end(), first) - features.begin(); 84 5304 : int index_second = find(features.begin(), features.end(), second) - features.begin(); 85 5304 : double accumulated = 0; 86 31440 : for (int value = 0; value < classNumStates; ++value) { 87 104544 : auto mask = samples.index({ -1, "..." }) == value; 88 78408 : auto first_dataset = samples.index({ index_first, mask }); 89 78408 : auto second_dataset = samples.index({ index_second, mask }); 90 52272 : auto weights_dataset = weights.index({ mask }); 91 52272 : auto mi = mutualInformation(first_dataset, second_dataset, weights_dataset); 92 26136 : auto pb = margin[value].item<double>(); 93 26136 : accumulated += pb * mi; 94 26136 : } 95 5304 : result.push_back(accumulated); 96 5304 : } 97 204 : long n_vars = source.size(); 98 204 : auto matrix = torch::zeros({ n_vars, n_vars }); 99 204 : auto indices = torch::triu_indices(n_vars, n_vars, 1); 100 5508 : for (auto i = 0; i < result.size(); ++i) { 101 5304 : auto x = indices[0][i]; 102 5304 : auto y = indices[1][i]; 103 5304 : matrix[x][y] = result[i]; 104 5304 : matrix[y][x] = result[i]; 105 5304 : } 106 408 : return matrix; 107 131784 : } 108 50295 : double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights) 109 : { 110 50295 : torch::Tensor counts = feature.bincount(weights); 111 50295 : double totalWeight = counts.sum().item<double>(); 112 50295 : torch::Tensor probs = counts.to(torch::kFloat) / totalWeight; 113 50295 : torch::Tensor logProbs = torch::log(probs); 114 50295 : torch::Tensor entropy = -probs * logProbs; 115 100590 : return entropy.nansum().item<double>(); 116 50295 : } 117 : // H(Y|X) = sum_{x in X} p(x) H(Y|X=x) 118 44793 : double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights) 119 : { 120 44793 : int numSamples = firstFeature.sizes()[0]; 121 44793 : torch::Tensor featureCounts = secondFeature.bincount(weights); 122 44793 : std::unordered_map<int, std::unordered_map<int, double>> jointCounts; 123 44793 : double totalWeight = 0; 124 8954403 : for (auto i = 0; i < numSamples; i++) { 125 8909610 : jointCounts[secondFeature[i].item<int>()][firstFeature[i].item<int>()] += weights[i].item<double>(); 126 8909610 : totalWeight += weights[i].item<float>(); 127 : } 128 44793 : if (totalWeight == 0) 129 0 : return 0; 130 44793 : double entropyValue = 0; 131 222747 : for (int value = 0; value < featureCounts.sizes()[0]; ++value) { 132 177954 : double p_f = featureCounts[value].item<double>() / totalWeight; 133 177954 : double entropy_f = 0; 134 601680 : for (auto& [label, jointCount] : jointCounts[value]) { 135 423726 : double p_l_f = jointCount / featureCounts[value].item<double>(); 136 423726 : if (p_l_f > 0) { 137 423726 : entropy_f -= p_l_f * log(p_l_f); 138 : } else { 139 0 : entropy_f = 0; 140 : } 141 : } 142 177954 : entropyValue += p_f * entropy_f; 143 : } 144 44793 : return entropyValue; 145 44793 : } 146 : // I(X;Y) = H(Y) - H(Y|X) 147 44793 : double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights) 148 : { 149 44793 : return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights); 150 : } 151 : /* 152 : Compute the maximum spanning tree considering the weights as distances 153 : and the indices of the weights as nodes of this square matrix using 154 : Kruskal algorithm 155 : */ 156 174 : std::vector<std::pair<int, int>> Metrics::maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root) 157 : { 158 174 : auto mst = MST(features, weights, root); 159 348 : return mst.maximumSpanningTree(); 160 174 : } 161 : } |
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