Create hyperparameter block_update

bayesnet/ensembles/BoostAODE.cc

@@ -16,7 +16,7 @@ namespace bayesnet {
     {
         validHyperparameters = {
             "maxModels", "bisection", "order", "convergence", "threshold",
-            "select_features", "maxTolerance", "predict_voting"
+            "select_features", "maxTolerance", "predict_voting", "block_update"
         };
 
     }
@@ -94,6 +94,10 @@ namespace bayesnet {
             }
             hyperparameters.erase("select_features");
         }
+        if (hyperparameters.contains("block_update")) {
+            block_update = hyperparameters["block_update"];
+            hyperparameters.erase("block_update");
+        }
         Classifier::setHyperparameters(hyperparameters);
     }
     std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)

bayesnet/ensembles/BoostAODE.h

@@ -28,14 +28,15 @@ namespace bayesnet {
         std::vector<int> initializeModels();
         torch::Tensor X_train, y_train, X_test, y_test;
         // Hyperparameters
-        bool bisection = false; // if true, use bisection stratety to add k models at once to the ensemble
-        int maxTolerance = 1;
+        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 = false; //if true, stop when the model does not improve
+        bool convergence = true; //if true, stop when the model does not improve
         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 = true;
     };
 }
 #endif