Create Boost class as Boost<x> classifiers parent

bayesnet/ensembles/BoostAODE.h

@@ -6,36 +6,21 @@
 
 #ifndef BOOSTAODE_H
 #define BOOSTAODE_H
-#include <map>
+#include <string>
+#include <vector>
 #include "bayesnet/classifiers/SPODE.h"
-#include "bayesnet/feature_selection/FeatureSelect.h"
-#include "boost.h"
-#include "Ensemble.h"
+#include "Boost.h"
 namespace bayesnet {
-    class BoostAODE : public Ensemble {
+    class BoostAODE : public Boost {
     public:
         explicit 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::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
         std::vector<int> initializeModels();
-        torch::Tensor X_train, y_train, X_test, y_test;
-        // Hyperparameters
-        bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
-        int maxTolerance = 3;
-        std::string order_algorithm; // order to process the KBest features asc, desc, rand
-        bool convergence = true; //if true, stop when the model does not improve
-        bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
-        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;
-        bool block_update = false;
     };
 }
 #endif