37 lines
1.8 KiB
C++
37 lines
1.8 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 {
|
|
class BoostAODE : public Ensemble {
|
|
public:
|
|
BoostAODE(bool predict_voting = true);
|
|
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::unordered_set<int> initializeModels();
|
|
torch::Tensor ensemble_predict(torch::Tensor& X, SPODE* model);
|
|
torch::Tensor dataset_;
|
|
torch::Tensor X_train, y_train, X_test, y_test;
|
|
// Hyperparameters
|
|
bool repeatSparent = false; // if true, a feature can be selected more than once
|
|
int maxModels = 0;
|
|
int tolerance = 0;
|
|
bool predict_single = true; // wether the last model is used to predict in training or the whole ensemble
|
|
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 = "desc"; // Selected feature selection algorithm
|
|
bool initialize_prob_table; // if true, initialize the prob_table with the first model (used in train)
|
|
torch::Tensor prob_table; // Table of probabilities for ensemble predicting if predict_single is false
|
|
FeatureSelect* featureSelector = nullptr;
|
|
double threshold = -1;
|
|
};
|
|
}
|
|
#endif |