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Add selectKParis method

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This commit is contained in:
Ricardo Montañana Gómez
2024-05-16 11:17:21 +02:00
parent 8784a24898
commit 2e3e0e0fc2
8 changed files with 224 additions and 26 deletions
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38
bayesnet/utils/BayesMetrics.cc
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@@ -30,6 +30,44 @@ namespace bayesnet {
}
samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
}
std::vector<std::pair<int, int>> Metrics::SelectKPairs(const torch::Tensor& weights, bool ascending, unsigned k)
{
// Return the K Best features
auto n = features.size();
if (k == 0) {
k = n;
}
// compute scores
scoresKPairs.clear();
pairsKBest.clear();
auto label = samples.index({ -1, "..." });
// for (int i = 0; i < n; ++i) {
// for (int j = i + 1; j < n; ++j) {
// scoresKBest.push_back(mutualInformation(samples.index({ i, "..." }), samples.index({ j, "..." }), weights));
// featuresKBest.push_back(i);
// featuresKBest.push_back(j);
// }
// }
// // sort & reduce scores and features
// if (ascending) {
// sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
// { return scoresKBest[i] < scoresKBest[j]; });
// sort(scoresKBest.begin(), scoresKBest.end(), std::less<double>());
// if (k < n) {
// for (int i = 0; i < n - k; ++i) {
// featuresKBest.erase(featuresKBest.begin());
// scoresKBest.erase(scoresKBest.begin());
// }
// }
// } else {
// sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
// { return scoresKBest[i] > scoresKBest[j]; });
// sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>());
// featuresKBest.resize(k);
// scoresKBest.resize(k);
// }
return pairsKBest;
}
std::vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k)
{
// Return the K Best features

5
bayesnet/utils/BayesMetrics.h
View File

@@ -16,6 +16,7 @@ namespace bayesnet {
Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
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);
std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
std::vector<std::pair<int, int>> SelectKPairs(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
std::vector<double> getScoresKBest() const;
double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
@@ -41,7 +42,7 @@ namespace bayesnet {
}
return result;
}
template <class T>
template <class T>
T pop_first(std::vector<T>& v)
{
T temp = v[0];
@@ -52,6 +53,8 @@ namespace bayesnet {
int classNumStates = 0;
std::vector<double> scoresKBest;
std::vector<int> featuresKBest; // sorted indices of the features
std::vector<std::pair<int, int>> pairsKBest; // sorted indices of the pairs
std::map<std::pair<int, int>, double> scoresKPairs;
double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
};
}