Refactor library structure

bayesnet/ensembles/BoostAODE.h

@@ -0,0 +1,37 @@
+#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