Cleaning more TODOs

src/engine.cpp

@@ -85,7 +85,7 @@ void Engine<T>::print_progress(float percentage)
 
 
 template <ProgramType T>
-void Engine<T>::calculate_stats(const Dataset& d)
+void Engine<T>::calculate_stats()
 {
     int pop_size = 0;
     for (int island=0; island<params.num_islands; ++island)
@@ -311,8 +311,6 @@ void Engine<T>::run(Dataset &data)
 
     tf::Taskflow taskflow;
 
-    // TODO: get references to all classes ( so they can be captured by taskflow) (like some private getters and setters)
-
     //std::cout << "stop criteria is ready " << std::endl;
     // stop criteria 
     unsigned generation = 0;
@@ -447,18 +445,13 @@ void Engine<T>::run(Dataset &data)
             auto finish_gen = subflow.emplace([&]() {
                 bool updated_best = this->update_best(data);
 
-                // TODO: fix this code below (if needed. this is borrowed from feat)
-                // if ( (use_arch || params.verbosity>1) || !logfile.empty()) {
-                //     // set objectives to make sure they are reported in log/verbose/arch
-                //     #pragma omp parallel for
-                //     for (unsigned int i=0; i<pop.size(); ++i)
-                //         pop.individuals.at(i).set_obj(params.objectives);
-                // }
+                // TODO: use_arch
+                if ( params.verbosity>1 || !logfile.empty()) {
+                    calculate_stats();
+                }
 
                 // TODO: logger working
                 // logger.log("calculate stats...",2);
-                calculate_stats(data); // TODO: calculate stats only if archive, logstats, or verbosity (otherwise it is not used)
-                // TODO: calculate stats does not need dataset
 
                 // if (use_arch)  // TODO: archive
                 //     archive.update(pop,params);

src/engine.h

@@ -40,7 +40,7 @@ class Engine{
 
     // outputs a progress bar, filled according to @param percentage.
     void print_progress(float percentage);
-    void calculate_stats(const Dataset& d);
+    void calculate_stats();
     void print_stats(std::ofstream& log, float fraction);      
     void log_stats(std::ofstream& log);
 

src/eval/evaluation.cpp

@@ -13,8 +13,7 @@ void Evaluation<T>::update_fitness(Population<T>& pop,
                     bool fit,
                     bool validation
                     )
-{
-    //TODO: it could use the validation_loss     
+{   
     auto idxs = pop.get_island_indexes(island);
 
     for (unsigned i = 0; i<idxs.size(); ++i)

src/ind/fitness.h

@@ -77,7 +77,8 @@ struct Fitness {
         dominated.resize(0);
     }
 
-    // Hash function TODO: stop using it (i think only deap needs this)
+    // Hash function (deap requires individuals (and fitness by induction)
+    // to be hashable)
     size_t hash() const {
         std::size_t h = std::hash<vector<float>>{}(wvalues);
         return h;
@@ -96,7 +97,6 @@ struct Fitness {
         return wvalues;
     }
 
-    // TODO: debug size, it is giving weird values
     // Method to set values
     void set_values(vector<float>& v) {
         if (v.size() != weights.size()) {

src/ind/individual.h

@@ -30,7 +30,6 @@ class Individual{
 
     Individual()
     {
-        // TODO: better initialization of arguments
         objectives = {"error", "complexity"}; 
     };
 
@@ -45,12 +44,13 @@ class Individual{
         // program = SS.make_program<T>(params, params.max_depth, params.max_size);
     };
 
-    // fitness, objetives, complexity, etc. TODO: create intermediate  functions to  interact  with fitness and program?
+    // fitness, objetives, complexity, etc.
     void fit(Dataset& data) {
         program.fit(data);
         // this flag is used to avoid re-fitting an individual. the program is_fitted_ flag is used to perform checks (like in predict with weights). They are two different things and I think I;ll keep this way (individual is just a container to keep program and fitness together) 
         this->is_fitted_ = true;
     };
+
     auto predict(Dataset& data) { return program.predict(data); };
 
     // TODO: predict proba and classification related methods.