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Add tests to reach 90% coverage

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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
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4
bayesnet/utils/BayesMetrics.cc
View File

@@ -9,12 +9,12 @@ namespace bayesnet {
, classNumStates(classNumStates)
{
}
//samples is nxm std::vector used to fit the model
//samples is n+1xm std::vector used to fit the model
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)
: features(features)
, className(className)
, classNumStates(classNumStates)
, samples(torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32))
, samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32))
{
for (int i = 0; i < vsamples.size(); ++i) {
samples.index_put_({ i, "..." }, torch::tensor(vsamples[i], torch::kInt32));

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