rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Add tests to reach 90% coverage

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2024-04-08 00:13:59 +02:00
parent 46cb8d30eb
commit 0d6a081d01
13 changed files with 424 additions and 56 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
bayesnet/utils/BayesMetrics.h
View File

@@ -5,11 +5,16 @@
#include <torch/torch.h>
namespace bayesnet {
class Metrics {
private:
int classNumStates = 0;
std::vector<double> scoresKBest;
std::vector<int> featuresKBest; // sorted indices of the features
double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
public:
Metrics() = default;
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<double> getScoresKBest() const;
double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
std::vector<float> conditionalEdgeWeights(std::vector<float>& weights); // To use in Python
torch::Tensor conditionalEdge(const torch::Tensor& weights);
std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
protected:
torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
std::string className;
@@ -34,16 +39,11 @@ namespace bayesnet {
v.erase(v.begin());
return temp;
}
public:
Metrics() = default;
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<double> getScoresKBest() const;
double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
std::vector<float> conditionalEdgeWeights(std::vector<float>& weights); // To use in Python
torch::Tensor conditionalEdge(const torch::Tensor& weights);
std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
private:
int classNumStates = 0;
std::vector<double> scoresKBest;
std::vector<int> featuresKBest; // sorted indices of the features
double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
};
}
#endif