BayesNet/bayesnet/ensembles/BoostAODE.h

41 lines
1.6 KiB
C++

#ifndef BOOSTAODE_H
#define BOOSTAODE_H
#include <map>
#include "bayesnet/classifiers/SPODE.h"
#include "bayesnet/feature_selection/FeatureSelect.h"
#include "Ensemble.h"
namespace bayesnet {
struct {
std::string CFS = "CFS";
std::string FCBF = "FCBF";
std::string IWSS = "IWSS";
}SelectFeatures;
struct {
std::string ASC = "asc";
std::string DESC = "desc";
std::string RAND = "rand";
}Orders;
class BoostAODE : public Ensemble {
public:
BoostAODE(bool predict_voting = false);
virtual ~BoostAODE() = default;
std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
void setHyperparameters(const nlohmann::json& hyperparameters) override;
protected:
void buildModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights) override;
private:
std::vector<int> initializeModels();
torch::Tensor X_train, y_train, X_test, y_test;
// Hyperparameters
bool bisection = false; // if true, use bisection stratety to add k models at once to the ensemble
int maxTolerance = 1;
std::string order_algorithm; // order to process the KBest features asc, desc, rand
bool convergence = false; //if true, stop when the model does not improve
bool selectFeatures = false; // if true, use feature selection
std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
FeatureSelect* featureSelector = nullptr;
double threshold = -1;
};
}
#endif