evaluation.cpp

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#include "evaluation.h"
 
namespace Brush{   
namespace Eval{
 
 
// fitness of population
template<ProgramType T> 
void Evaluation<T>::update_fitness(Population<T>& pop,
                    int island,
                    const Dataset& data, 
                    const Parameters& params, 
                    bool fit,
                    bool validation
                    )
{   
    auto indices = pop.get_island_indexes(island);
 
    for (unsigned i = 0; i<indices.size(); ++i)
    {
        Individual<T>& ind = *pop.individuals.at(indices.at(i)).get(); // we are modifying it, so operator[] wont work
 
        if (false) // pass
        {
            ind.fitness.set_loss(MAX_FLT);
            ind.fitness.set_loss_v(MAX_FLT);
 
            ind.error = MAX_FLT*VectorXf::Ones(data.y.size());
        }
        else
        {
            // assign weights to individual
            if (fit && ind.get_is_fitted() == false)
            {
                ind.program.fit(data.get_training_data());
            }
 
            assign_fit(ind, data, params, validation);
        }
    }
}
 
// assign loss to program
template<ProgramType T> 
void Evaluation<T>::assign_fit(Individual<T>& ind, const Dataset& data, 
                               const Parameters& params, bool val)
{
    VectorXf errors;
    using PT = ProgramType;
    
    Dataset train = data.get_training_data();
    float f = S.score(ind, train, errors, params);
    ind.error = errors;
 
    float f_v = f;
    if (data.use_validation) {
        Dataset validation = data.get_validation_data();
 
        // when calculating validation score, we should not let
        // it write in errors vector. That would avoid validation data leakage
        VectorXf val_errors;
        f_v = S.score(ind, validation, val_errors, params);
 
        // if (val) // never use validation data here. This is used in lexicase selection
        //     ind.error = val_errors;
    }
 
    float error_weight = Individual<T>::weightsMap[params.scorer];
    if (std::isnan(f) || std::isinf(f))
        f = error_weight > 0 ? -MAX_FLT : MAX_FLT;
    if (std::isnan(f_v) || std::isinf(f_v))
        f_v = error_weight > 0 ? -MAX_FLT : MAX_FLT;
 
    // This is what is going to determine the weights for the individual's fitness.
    // OBS: always use get_objectives, as it will replace the "scorer" string with
    // the actual scorer function name
    ind.set_objectives(params.get_objectives());
 
    // when we use these setters, it updates its previous values references
    ind.fitness.set_loss(f);
    ind.fitness.set_loss_v(f_v);
    ind.fitness.set_size(ind.get_size());
    ind.fitness.set_complexity(ind.get_complexity());
    ind.fitness.set_linear_complexity(ind.get_linear_complexity());
    ind.fitness.set_depth(ind.get_depth());
 
    vector<float> values;
    values.resize(0);
 
    // TODO: implement a better way of switching between train and val
    // without the burden of calculating stuff everytime
    for (const auto& n : ind.get_objectives())
    {
        // TODO: this should be done in the fitness class when calling wvalues() or values()
        if (n.compare(params.scorer)==0)
            values.push_back(val ? f_v : f);
        else if (n.compare("complexity")==0)
            values.push_back(ind.get_complexity());
        else if (n.compare("linear_complexity")==0)
            values.push_back(ind.get_linear_complexity());
        else if (n.compare("size")==0)
            values.push_back(ind.get_size());
        else if (n.compare("depth")==0)
            values.push_back(ind.get_depth());
        else
            HANDLE_ERROR_THROW(n+" is not a known objective");
    }
    
    // will use inner attributes to set the fitness object
    ind.fitness.set_values(values); 
}
 
} // Pop
} // Brush