Comments

src/bandit/thompson.cpp

@@ -82,12 +82,12 @@ void ThompsonSamplingBandit<T>::update(T arm, float reward) {
     // reward must be either 0 or 1
 
     alphas[arm] += reward;
-    betas[arm]  += 1-reward;
+    betas[arm]  += 1.0f-reward;
 
     if (dynamic_update && alphas[arm] + betas[arm] >= C)
     {
-        alphas[arm] *= C/(C+1) ;
-        betas[arm]  *= C/(C+1) ;
+        alphas[arm] *= C/(C+1.0f) ;
+        betas[arm]  *= C/(C+1.0f) ;
     }
 }
 

src/data/data.h

@@ -72,6 +72,7 @@ class Dataset
 
         /// @brief dataset features, as key value pairs
         std::map<string, State> features;
+
         // TODO: this should probably be a more complex type to include feature type 
         // and potentially other info, like arbitrary relations between features
 
@@ -92,6 +93,7 @@ class Dataset
         bool use_batch;
 
         Dataset operator()(const vector<size_t>& idx) const;
+
         /// call init at the end of constructors
         /// to define metafeatures of the data.
         void init();
@@ -242,6 +244,18 @@ class Dataset
         /* template<> ArrayXb get<ArrayXb>(std::string name) */
 }; // class data
 
+// TODO: serialization of features in order to nlohmann to work
+// NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Dataset,
+//     features,
+//     y,
+//     classification,
+//     validation_size,
+//     use_validation,
+//     batch_size,
+//     use_batch,
+//     shuffle_split
+// );
+
 // // read csv
 // Dataset read_csv(const std::string & path, MatrixXf& X, VectorXf& y, 
 //     vector<string>& names, vector<char> &dtypes, bool& binary_endpoint, char sep) ;

src/engine.h

@@ -164,7 +164,7 @@ class Engine{
 };
 
 // TODO: should I serialize data and search space as well?
-// Only stuff to make new predictions or call fit again
+// Only stuff to make new predictions should be serialized
 NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Engine<PT::Regressor>, params, best_ind, archive);
 NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Engine<PT::BinaryClassifier>, params, best_ind, archive);
 NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Engine<PT::MulticlassClassifier>, params, best_ind, archive);

src/vary/variation.cpp

@@ -446,6 +446,8 @@ std::optional<Individual<T>> Variation<T>::cross(
                 Individual<T> ind(child);
                 ind.set_variation("cx"); // TODO: use enum here to make it faster
 
+                // std::cout << "returning after crossover" << std::endl;
+
                 return ind;
             }
         }
@@ -562,7 +564,8 @@ std::optional<Individual<T>> Variation<T>::mutate(const Individual<T>& parent)
         else // it must be"toggle_weight_off"
             success = ToggleWeightOffMutation::mutate(child.Tree, spot, search_space, parameters);
 
-        // std::cout << "returning" << std::endl;
+        // std::cout << "returning after mutation " << choice << std::endl;
+
         if (success
         && ( (child.size()  <= parameters.max_size)
         &&   (child.depth() <= parameters.max_depth) )){