Comments

src/bandit/bandit_operator.h

@@ -2,7 +2,7 @@
 #define BANDIT_OPERATOR_H
 
 #include "../init.h"
-#include "../data/data.h"
+// #include "../data/data.h"
 #include "../types.h"
 #include "../params.h"
 #include "../program/tree_node.h"

src/data/data.cpp

@@ -247,7 +247,7 @@ void Dataset::init()
                 [&](int element) { return element; });
     }
     else if (classification && true) // figuring out training and validation data indexes
-    { // Stratified split for classification problems. TODO: parameters to change stratify behavior? (false by default)
+    { // Stratified split for classification problems. TODO: parameters to change stratify behavior? (and set false by default)
         std::map<float, vector<int>> class_indices; // TODO: I think I can remove many std:: from the code..
         for (size_t i = 0; i < n_samples; ++i) {
             class_indices[y[i]].push_back(i);

src/vary/variation.cpp

@@ -760,79 +760,6 @@ void Variation<T>::vary(Population<T>& pop, int island,
     }
 };
 
-template<ProgramType T>
-vector<float> Variation<T>::calculate_rewards(Population<T>& pop, int island)
-{
-    // get island indexes
-    // go to the offspring of the island
-    // find which objectives we are using
-    // calculate the rewards based on variation of the objectives (must take into account the weights)
-    // define which criteria for rewards we are going to use (dominates, improved at least one without changing the other, etc)
-    // return the rewards vector, which will be used to update the bandits
-
-    vector<float> rewards;
-    rewards.resize(0);
-
-    // this is done assuming index of offspring is valid and the second half
-    // of indices is the offspring. This is done in vary() function.
-    auto indices = pop.get_island_indexes(island);
-
-    for (unsigned i = 0 ; i < indices.size()/2; ++i)
-    {
-        if (pop.individuals.at(indices.at(indices.size()/2 + i)) == nullptr)
-        {
-            HANDLE_ERROR_THROW("bad loop index in calculate_rewards");
-        }
-
-        const Individual<T>& ind = *pop.individuals.at(
-            indices.at(indices.size()/2 + i) );
-
-        vector<float> deltas(ind.get_objectives().size(), 0.0f);
-
-        float delta = 0.0f;
-        float weight = 0.0f;
-
-        for (const auto& obj : ind.get_objectives())
-        {   
-            // TODO: make this a function? I think this code below is repeated in other places
-            // multiply by the weight so it is a maximization problem regardless of obj
-            if (obj.compare(parameters.scorer)==0)
-                delta = ind.fitness.get_loss()-ind.fitness.get_prev_loss();
-            else if (obj.compare("complexity")==0)
-                delta = ind.fitness.get_complexity()-ind.fitness.get_prev_complexity();
-            else if (obj.compare("linear_complexity")==0)
-                delta = ind.fitness.get_linear_complexity()-ind.fitness.get_prev_linear_complexity();
-            else if (obj.compare("size")==0)
-                delta = ind.fitness.get_size()-ind.fitness.get_prev_size();
-            else if (obj.compare("depth")==0)
-                delta = ind.fitness.get_depth()-ind.fitness.get_prev_depth();
-            else
-                HANDLE_ERROR_THROW(obj+" is not a known objective");
-
-            auto it = Individual<T>::weightsMap.find(obj);
-            weight = it->second;
-
-            deltas.push_back(delta*weight);
-        }
-
-        // calculate the rewards based on the objectives
-        bool allPositive = true;
-        for (float d : deltas) {
-            if (d < 0) {
-                allPositive = false;
-                break;
-            }
-        }
-
-        if (allPositive)
-            rewards.push_back(1.0);
-        else
-            rewards.push_back(0.0);
-    }
-
-    return rewards;
-};
-
 template <Brush::ProgramType T>
 void Variation<T>::update_ss()
 {

src/vary/variation.h

@@ -181,15 +181,6 @@ class Variation {
      */
     void vary(Population<T>& pop, int island, const vector<size_t>& parents);
 
-    /**
-     * Calculates the rewards for the given population and island.
-     *
-     * @param pop The population to calculate rewards for.
-     * @param island The island index.
-     * @return A vector of rewards for the population.
-     */
-    vector<float> calculate_rewards(Population<T>& pop, int island);
-
     /**
      * Updates the probability distribution sampling for variation and nodes based on the given rewards.
      *