Extract buildModel method to parent class in Boost

bayesnet/ensembles/Boost.cc

@@ -3,6 +3,7 @@
 // SPDX-FileType: SOURCE
 // SPDX-License-Identifier: MIT
 // ***************************************************************
+#include <folding.hpp>
 #include "bayesnet/feature_selection/CFS.h"
 #include "bayesnet/feature_selection/FCBF.h"
 #include "bayesnet/feature_selection/IWSS.h"
@@ -67,6 +68,37 @@ namespace bayesnet {
         }
         Classifier::setHyperparameters(hyperparameters);
     }
+    void Boost::buildModel(const torch::Tensor& weights)
+    {
+        // Models shall be built in trainModel
+        models.clear();
+        significanceModels.clear();
+        n_models = 0;
+        // Prepare the validation dataset
+        auto y_ = dataset.index({ -1, "..." });
+        if (convergence) {
+            // Prepare train & validation sets from train data
+            auto fold = folding::StratifiedKFold(5, y_, 271);
+            auto [train, test] = fold.getFold(0);
+            auto train_t = torch::tensor(train);
+            auto test_t = torch::tensor(test);
+            // Get train and validation sets
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
+            y_train = dataset.index({ -1, train_t });
+            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
+            y_test = dataset.index({ -1, test_t });
+            dataset = X_train;
+            m = X_train.size(1);
+            auto n_classes = states.at(className).size();
+            // Build dataset with train data
+            buildDataset(y_train);
+            metrics = Metrics(dataset, features, className, n_classes);
+        } else {
+            // Use all data to train
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
+            y_train = y_;
+        }
+    }
     std::vector<int> Boost::featureSelection(torch::Tensor& weights_)
     {
         int maxFeatures = 0;

bayesnet/ensembles/Boost.h

@@ -31,6 +31,7 @@ namespace bayesnet {
         void setHyperparameters(const nlohmann::json& hyperparameters_) override;
     protected:
         std::vector<int> featureSelection(torch::Tensor& weights_);
+        void buildModel(const torch::Tensor& weights) override;
         std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights);
         std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
         torch::Tensor X_train, y_train, X_test, y_test;

bayesnet/ensembles/BoostA2DE.cc

@@ -18,38 +18,6 @@ namespace bayesnet {
 
     BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
     {
-    }
-    void BoostA2DE::buildModel(const torch::Tensor& weights)
-    {
-        // Models shall be built in trainModel
-        models.clear();
-        significanceModels.clear();
-        n_models = 0;
-        // Prepare the validation dataset
-        auto y_ = dataset.index({ -1, "..." });
-        if (convergence) {
-            // Prepare train & validation sets from train data
-            auto fold = folding::StratifiedKFold(5, y_, 271);
-            auto [train, test] = fold.getFold(0);
-            auto train_t = torch::tensor(train);
-            auto test_t = torch::tensor(test);
-            // Get train and validation sets
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
-            y_train = dataset.index({ -1, train_t });
-            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
-            y_test = dataset.index({ -1, test_t });
-            dataset = X_train;
-            m = X_train.size(1);
-            auto n_classes = states.at(className).size();
-            // Build dataset with train data
-            buildDataset(y_train);
-            metrics = Metrics(dataset, features, className, n_classes);
-        } else {
-            // Use all data to train
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-            y_train = y_;
-        }
-
     }
     void BoostA2DE::trainModel(const torch::Tensor& weights)
     {

bayesnet/ensembles/BoostA2DE.h

@@ -17,7 +17,6 @@ namespace bayesnet {
         virtual ~BoostA2DE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
     protected:
-        void buildModel(const torch::Tensor& weights) override;
         void trainModel(const torch::Tensor& weights) override;
     };
 }

bayesnet/ensembles/BoostAODE.cc

@@ -4,11 +4,11 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
+#include <random> 
 #include <set>
 #include <functional>
 #include <limits.h>
 #include <tuple>
-#include <folding.hpp>
 #include "BoostAODE.h"
 #include "lib/log/loguru.cpp"
 
@@ -17,37 +17,7 @@ namespace bayesnet {
     BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
     {
     }
-    void BoostAODE::buildModel(const torch::Tensor& weights)
+
-    {
-        // Models shall be built in trainModel
-        models.clear();
-        significanceModels.clear();
-        n_models = 0;
-        // Prepare the validation dataset
-        auto y_ = dataset.index({ -1, "..." });
-        if (convergence) {
-            // Prepare train & validation sets from train data
-            auto fold = folding::StratifiedKFold(5, y_, 271);
-            auto [train, test] = fold.getFold(0);
-            auto train_t = torch::tensor(train);
-            auto test_t = torch::tensor(test);
-            // Get train and validation sets
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
-            y_train = dataset.index({ -1, train_t });
-            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
-            y_test = dataset.index({ -1, test_t });
-            dataset = X_train;
-            m = X_train.size(1);
-            auto n_classes = states.at(className).size();
-            // Build dataset with train data
-            buildDataset(y_train);
-            metrics = Metrics(dataset, features, className, n_classes);
-        } else {
-            // Use all data to train
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-            y_train = y_;
-        }
-    }
     std::vector<int> BoostAODE::initializeModels()
     {
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);

bayesnet/ensembles/BoostAODE.h

@@ -17,7 +17,6 @@ namespace bayesnet {
         virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
     protected:
-        void buildModel(const torch::Tensor& weights) override;
         void trainModel(const torch::Tensor& weights) override;
     private:
         std::vector<int> initializeModels();