Add hyperparameter to ChangeLog and Boost class

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+
+- Add a new hyperparameter to the BoostAODE class, *alphablock*, to control the way α is computed, with the last model or with the ensmble built so far. Default value is *false*.
+
 ## [1.0.6] 2024-11-23
 
 ### Fixed

bayesnet/ensembles/Boost.cc

@@ -12,7 +12,7 @@
 namespace bayesnet {
     Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
     {
-        validHyperparameters = { "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
+        validHyperparameters = { "alpha_block", "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
         "predict_voting", "select_features", "block_update" };
     }
     void Boost::setHyperparameters(const nlohmann::json& hyperparameters_)
@@ -26,6 +26,10 @@ namespace bayesnet {
             }
             hyperparameters.erase("order");
         }
+        if (hyperparameters.contains("alpha_block")) {
+            alpha_block = hyperparameters["alpha_block"];
+            hyperparameters.erase("alpha_block");
+        }
         if (hyperparameters.contains("convergence")) {
             convergence = hyperparameters["convergence"];
             hyperparameters.erase("convergence");
@@ -66,6 +70,12 @@ namespace bayesnet {
             block_update = hyperparameters["block_update"];
             hyperparameters.erase("block_update");
         }
+        if (block_update && alpha_block) {
+            throw std::invalid_argument("alpha_block and block_update cannot be true at the same time");
+        }
+        if (block_update && !bisection) {
+            throw std::invalid_argument("block_update needs bisection to be true");
+        }
         Classifier::setHyperparameters(hyperparameters);
     }
     void Boost::buildModel(const torch::Tensor& weights)

bayesnet/ensembles/Boost.h

@@ -45,8 +45,8 @@ namespace bayesnet {
         std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
         FeatureSelect* featureSelector = nullptr;
         double threshold = -1;
-        bool block_update = false;
-
+        bool block_update = false; // if true, use block update algorithm, only meaningful if bisection is true
+        bool alpha_block = false; // if true, the alpha is computed with the ensemble built so far and the new model
     };
 }
 #endif