27 KiB
27 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 11 338 : Metrics::Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates) 12 338 : : samples(samples) 13 338 : , features(features) 14 338 : , className(className) 15 338 : , classNumStates(classNumStates) 16 : { 17 338 : } 18 : //samples is n+1xm std::vector used to fit the model 19 16 : 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 16 : : features(features) 21 16 : , className(className) 22 16 : , classNumStates(classNumStates) 23 32 : , samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32)) 24 : { 25 128 : for (int i = 0; i < vsamples.size(); ++i) { 26 448 : samples.index_put_({ i, "..." }, torch::tensor(vsamples[i], torch::kInt32)); 27 : } 28 64 : samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32)); 29 144 : } 30 90 : std::vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k) 31 : { 32 : // Return the K Best features 33 90 : auto n = features.size(); 34 90 : if (k == 0) { 35 0 : k = n; 36 : } 37 : // compute scores 38 90 : scoresKBest.clear(); 39 90 : featuresKBest.clear(); 40 270 : auto label = samples.index({ -1, "..." }); 41 2837 : for (int i = 0; i < n; ++i) { 42 8241 : scoresKBest.push_back(mutualInformation(label, samples.index({ i, "..." }), weights)); 43 2747 : featuresKBest.push_back(i); 44 : } 45 : // sort & reduce scores and features 46 90 : if (ascending) { 47 19 : sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j) 48 453 : { return scoresKBest[i] < scoresKBest[j]; }); 49 19 : sort(scoresKBest.begin(), scoresKBest.end(), std::less<double>()); 50 19 : if (k < n) { 51 28 : for (int i = 0; i < n - k; ++i) { 52 20 : featuresKBest.erase(featuresKBest.begin()); 53 20 : scoresKBest.erase(scoresKBest.begin()); 54 : } 55 : } 56 : } else { 57 71 : sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j) 58 12619 : { return scoresKBest[i] > scoresKBest[j]; }); 59 71 : sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>()); 60 71 : featuresKBest.resize(k); 61 71 : scoresKBest.resize(k); 62 : } 63 180 : return featuresKBest; 64 2927 : } 65 8 : std::vector<double> Metrics::getScoresKBest() const 66 : { 67 8 : return scoresKBest; 68 : } 69 : 70 34 : torch::Tensor Metrics::conditionalEdge(const torch::Tensor& weights) 71 : { 72 34 : auto result = std::vector<double>(); 73 34 : auto source = std::vector<std::string>(features); 74 34 : source.push_back(className); 75 34 : auto combinations = doCombinations(source); 76 : // Compute class prior 77 34 : auto margin = torch::zeros({ classNumStates }, torch::kFloat); 78 184 : for (int value = 0; value < classNumStates; ++value) { 79 600 : auto mask = samples.index({ -1, "..." }) == value; 80 150 : margin[value] = mask.sum().item<double>() / samples.size(1); 81 150 : } 82 918 : for (auto [first, second] : combinations) { 83 884 : int index_first = find(features.begin(), features.end(), first) - features.begin(); 84 884 : int index_second = find(features.begin(), features.end(), second) - features.begin(); 85 884 : double accumulated = 0; 86 5240 : for (int value = 0; value < classNumStates; ++value) { 87 17424 : auto mask = samples.index({ -1, "..." }) == value; 88 13068 : auto first_dataset = samples.index({ index_first, mask }); 89 13068 : auto second_dataset = samples.index({ index_second, mask }); 90 8712 : auto weights_dataset = weights.index({ mask }); 91 8712 : auto mi = mutualInformation(first_dataset, second_dataset, weights_dataset); 92 4356 : auto pb = margin[value].item<double>(); 93 4356 : accumulated += pb * mi; 94 4356 : } 95 884 : result.push_back(accumulated); 96 884 : } 97 34 : long n_vars = source.size(); 98 34 : auto matrix = torch::zeros({ n_vars, n_vars }); 99 34 : auto indices = torch::triu_indices(n_vars, n_vars, 1); 100 918 : for (auto i = 0; i < result.size(); ++i) { 101 884 : auto x = indices[0][i]; 102 884 : auto y = indices[1][i]; 103 884 : matrix[x][y] = result[i]; 104 884 : matrix[y][x] = result[i]; 105 884 : } 106 68 : return matrix; 107 21964 : } 108 : // To use in Python 109 0 : std::vector<float> Metrics::conditionalEdgeWeights(std::vector<float>& weights_) 110 : { 111 0 : const torch::Tensor weights = torch::tensor(weights_); 112 0 : auto matrix = conditionalEdge(weights); 113 0 : std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel()); 114 0 : return v; 115 0 : } 116 8506 : double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights) 117 : { 118 8506 : torch::Tensor counts = feature.bincount(weights); 119 8506 : double totalWeight = counts.sum().item<double>(); 120 8506 : torch::Tensor probs = counts.to(torch::kFloat) / totalWeight; 121 8506 : torch::Tensor logProbs = torch::log(probs); 122 8506 : torch::Tensor entropy = -probs * logProbs; 123 17012 : return entropy.nansum().item<double>(); 124 8506 : } 125 : // H(Y|X) = sum_{x in X} p(x) H(Y|X=x) 126 7684 : double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights) 127 : { 128 7684 : int numSamples = firstFeature.sizes()[0]; 129 7684 : torch::Tensor featureCounts = secondFeature.bincount(weights); 130 7684 : std::unordered_map<int, std::unordered_map<int, double>> jointCounts; 131 7684 : double totalWeight = 0; 132 991784 : for (auto i = 0; i < numSamples; i++) { 133 984100 : jointCounts[secondFeature[i].item<int>()][firstFeature[i].item<int>()] += weights[i].item<double>(); 134 984100 : totalWeight += weights[i].item<float>(); 135 : } 136 7684 : if (totalWeight == 0) 137 0 : return 0; 138 7684 : double entropyValue = 0; 139 26852 : for (int value = 0; value < featureCounts.sizes()[0]; ++value) { 140 19168 : double p_f = featureCounts[value].item<double>() / totalWeight; 141 19168 : double entropy_f = 0; 142 57129 : for (auto& [label, jointCount] : jointCounts[value]) { 143 37961 : double p_l_f = jointCount / featureCounts[value].item<double>(); 144 37961 : if (p_l_f > 0) { 145 37961 : entropy_f -= p_l_f * log(p_l_f); 146 : } else { 147 0 : entropy_f = 0; 148 : } 149 : } 150 19168 : entropyValue += p_f * entropy_f; 151 : } 152 7684 : return entropyValue; 153 7684 : } 154 : // I(X;Y) = H(Y) - H(Y|X) 155 7684 : double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights) 156 : { 157 7684 : return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights); 158 : } 159 : /* 160 : Compute the maximum spanning tree considering the weights as distances 161 : and the indices of the weights as nodes of this square matrix using 162 : Kruskal algorithm 163 : */ 164 29 : std::vector<std::pair<int, int>> Metrics::maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root) 165 : { 166 29 : auto mst = MST(features, weights, root); 167 58 : return mst.maximumSpanningTree(); 168 29 : } 169 : } |
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| Generated by: LCOV version 2.0-1 |
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