CrossValidator.cpp

//
// Created by martin on 14.11.18.
//

#include "CrossValidator.h"
#include "message.h"

namespace lib4neuro {
    LIB4NEURO_API CrossValidator::CrossValidator(ILearningMethods* optimizer, ErrorFunction* ef) {
        this->optimizer = optimizer;
        this->ef = ef;
    }

    LIB4NEURO_API void CrossValidator::run_k_fold_test(unsigned int k, unsigned int tests_number, std::ofstream* results_file_path) {
        //TODO do not duplicate code - write in a more elegant way
        NeuralNetwork *net = this->ef->get_network_instance();

        double cv_err_sum = 0;

        for(unsigned int i = 0; i < tests_number; i++) {
            COUT_INFO("Cross-validation run " << i+1 << std::endl);
            *results_file_path << "Cross-validation run " << i+1 << std::endl;

            this->ef->divide_data_train_test(1.0/k);
            *results_file_path << "Number of train data points: " << this->ef->get_dataset()->get_n_elements() << std::endl;
            *results_file_path << "Number of test data points: " << this->ef->get_test_dataset()->get_n_elements() << std::endl;
            net->randomize_parameters();
            net->scale_parameters( 1.0 / (net->get_n_weights() + net->get_n_biases()));
            this->optimizer->optimize(*this->ef);

            /* Error evaluation and writing */
            double err = this->ef->eval_on_test_data(results_file_path);
            cv_err_sum += err;
            COUT_INFO("CV error (run " << i+1 << "): " << err << std::endl << std::endl);

            this->ef->return_full_data_set_for_training();
        }

        COUT_INFO("CV error mean: " << cv_err_sum/tests_number << std::endl);
        *results_file_path << "CV error mean: " << cv_err_sum/tests_number << std::endl;
    }

    LIB4NEURO_API void CrossValidator::run_k_fold_test(unsigned int k, unsigned int tests_number, std::string results_file_path) {
        NeuralNetwork *net = this->ef->get_network_instance();

        double cv_err_sum = 0;

        for(unsigned int i = 0; i < tests_number; i++) {
            COUT_INFO("Cross-validation run " << i+1 << std::endl);

            this->ef->divide_data_train_test(1.0/k);
            COUT_DEBUG("Number of train data points: " << this->ef->get_dataset()->get_n_elements() << std::endl);
            COUT_DEBUG("Number of test data points: " << this->ef->get_test_dataset()->get_n_elements() << std::endl);
            net->randomize_parameters();
            net->scale_parameters( 1.0 / (net->get_n_weights() + net->get_n_biases()));
            this->optimizer->optimize(*this->ef);

            /* Error evaluation and writing */
            double err;
            if(results_file_path == "") {
                err = this->ef->eval_on_test_data();
            } else {
                err = this->ef->eval_on_test_data(results_file_path + "_cv" + std::to_string(i) + ".dat");
            }
            cv_err_sum += err;
            COUT_INFO("CV error (run " << i+1 << "): " << err << std::endl << std::endl);

            this->ef->return_full_data_set_for_training();
        }

        COUT_INFO("CV error mean: " << cv_err_sum/tests_number << std::endl);
    }
}