49 lines
2.0 KiB
C++
49 lines
2.0 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#ifndef BOOSTAODE_H
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#define BOOSTAODE_H
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#include <map>
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#include "bayesnet/classifiers/SPODE.h"
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#include "bayesnet/feature_selection/FeatureSelect.h"
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#include "Ensemble.h"
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namespace bayesnet {
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struct {
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std::string CFS = "CFS";
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std::string FCBF = "FCBF";
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std::string IWSS = "IWSS";
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}SelectFeatures;
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struct {
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std::string ASC = "asc";
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std::string DESC = "desc";
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std::string RAND = "rand";
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}Orders;
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class BoostAODE : public Ensemble {
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public:
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BoostAODE(bool predict_voting = false);
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virtual ~BoostAODE() = default;
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std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
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void setHyperparameters(const nlohmann::json& hyperparameters_) override;
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protected:
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void buildModel(const torch::Tensor& weights) override;
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void trainModel(const torch::Tensor& weights) override;
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private:
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std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
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std::vector<int> initializeModels();
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torch::Tensor X_train, y_train, X_test, y_test;
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// Hyperparameters
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bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
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int maxTolerance = 3;
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std::string order_algorithm; // order to process the KBest features asc, desc, rand
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bool convergence = true; //if true, stop when the model does not improve
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bool selectFeatures = false; // if true, use feature selection
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std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
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FeatureSelect* featureSelector = nullptr;
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double threshold = -1;
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bool block_update = false;
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};
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}
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#endif |