68 lines
2.7 KiB
C++
68 lines
2.7 KiB
C++
#include <sstream>
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#include <algorithm>
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#include <numeric>
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#include <utility>
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#include "RocAuc.h"
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namespace platform {
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double RocAuc::compute(const torch::Tensor& y_proba, const torch::Tensor& labels)
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{
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size_t nClasses = y_proba.size(1);
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// In binary classification problem there's no need to calculate the average of the AUCs
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if (nClasses == 2)
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nClasses = 1;
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size_t nSamples = y_proba.size(0);
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y_test = tensorToVector(labels);
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std::vector<double> aucScores(nClasses, 0.0);
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for (size_t classIdx = 0; classIdx < nClasses; ++classIdx) {
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scoresAndLabels.clear();
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for (size_t i = 0; i < nSamples; ++i) {
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scoresAndLabels.emplace_back(y_proba[i][classIdx].item<float>(), y_test[i] == classIdx ? 1 : 0);
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}
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aucScores[classIdx] = compute_common(nSamples, classIdx);
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}
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return std::accumulate(aucScores.begin(), aucScores.end(), 0.0) / nClasses;
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}
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double RocAuc::compute(const std::vector<std::vector<double>>& y_proba, const std::vector<int>& labels)
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{
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y_test = labels;
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size_t nClasses = y_proba[0].size();
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// In binary classification problem there's no need to calculate the average of the AUCs
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if (nClasses == 2)
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nClasses = 1;
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size_t nSamples = y_proba.size();
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std::vector<double> aucScores(nClasses, 0.0);
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for (size_t classIdx = 0; classIdx < nClasses; ++classIdx) {
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scoresAndLabels.clear();
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for (size_t i = 0; i < nSamples; ++i) {
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scoresAndLabels.emplace_back(y_proba[i][classIdx], labels[i] == classIdx ? 1 : 0);
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}
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aucScores[classIdx] = compute_common(nSamples, classIdx);
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}
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return std::accumulate(aucScores.begin(), aucScores.end(), 0.0) / nClasses;
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}
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double RocAuc::compute_common(size_t nSamples, size_t classIdx)
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{
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std::sort(scoresAndLabels.begin(), scoresAndLabels.end(), std::greater<>());
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std::vector<double> tpr, fpr;
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double tp = 0, fp = 0;
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double totalPos = std::count(y_test.begin(), y_test.end(), classIdx);
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double totalNeg = nSamples - totalPos;
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if (totalPos == 0 || totalNeg == 0) return 0.5; // neutral AUC
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for (const auto& [score, label] : scoresAndLabels) {
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if (label == 1) {
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tp += 1;
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} else {
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fp += 1;
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}
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tpr.push_back(tp / totalPos);
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fpr.push_back(fp / totalNeg);
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}
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double auc = 0.0;
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for (size_t i = 1; i < tpr.size(); ++i) {
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auc += 0.5 * (fpr[i] - fpr[i - 1]) * (tpr[i] + tpr[i - 1]);
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}
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return auc;
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}
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} |