dlib C++ Library - auto.cpp

    // Copyright (C) 2018  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_AUTO_LEARnING_CPP_
    #define DLIB_AUTO_LEARnING_CPP_

    #include "auto.h"
    #include "../global_optimization.h"
    #include "svm_c_trainer.h"

    #include <iostream>
    #include <thread>

    namespace dlib
    {

        normalized_function<decision_function<radial_basis_kernel<matrix<double,0,1>>>> auto_train_rbf_classifier (
            std::vector<matrix<double,0,1>> x,
            std::vector<double> y,
            const std::chrono::nanoseconds max_runtime,
            bool be_verbose
        )
        {
            const auto num_positive_training_samples = sum(mat(y)>0);
            const auto num_negative_training_samples = sum(mat(y)<0);
            DLIB_CASSERT(num_positive_training_samples >= 6 && num_negative_training_samples >= 6,
                "You must provide at least 6 examples of each class to this training routine.");
            // make sure requires clause is not broken
            DLIB_CASSERT(is_binary_classification_problem(x,y) == true,
                "\tdecision_function svm_c_trainer::train(x,y)"
                << "\n\t invalid inputs were given to this function"
                << "\n\t x.size(): " << x.size()
                << "\n\t y.size(): " << y.size()
                << "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
            );

            randomize_samples(x,y);

            using kernel_type = radial_basis_kernel<matrix<double,0,1>>;
            normalized_function<decision_function<kernel_type>> df;
            // let the normalizer learn the mean and standard deviation of the samples
            df.normalizer.train(x);
            for (auto& samp : x)
                samp = df.normalizer(samp);

            std::mutex m;
            auto cross_validation_score = [&](const double gamma, const double c1, const double c2)
            {
                svm_c_trainer<kernel_type> trainer;
                trainer.set_kernel(kernel_type(gamma));
                trainer.set_c_class1(c1);
                trainer.set_c_class2(c2);

                // Finally, perform 6-fold cross validation and then print and return the results.
                matrix<double> result = cross_validate_trainer(trainer, x, y, 6);
                if (be_verbose)
                {
                    std::lock_guard<std::mutex> lock(m);
                    std::cout << "gamma: " << std::setw(11) << gamma << "  c1: " << std::setw(11) << c1 <<  "  c2: " << std::setw(11) << c2 <<  "  cross validation accuracy: " << result << std::flush;
                }

                // return the f1 score plus a penalty for picking large parameter settings
                // since those are, a priori less likely to generalize.
                return 2*prod(result)/sum(result) - std::max(c1,c2)/1e12 - gamma/1e8;
            };

            if (be_verbose)
                std::cout << "Searching for best RBF-SVM training parameters..." << std::endl;
            auto result = find_max_global(
                default_thread_pool(),
                cross_validation_score,
                {1e-5, 1e-5, 1e-5},  // lower bound constraints on gamma, c1, and c2, respectively
                {100,  1e6,  1e6},   // upper bound constraints on gamma, c1, and c2, respectively
                max_runtime);

            double best_gamma = result.x(0);
            double best_c1    = result.x(1);
            double best_c2    = result.x(2);

            if (be_verbose)
            {
                std::cout << " best cross-validation score: " << result.y << std::endl;
                std::cout << " best gamma: " << best_gamma << "   best c1: " << best_c1 << "    best c2: "<< best_c2  << std::endl;
            }

            svm_c_trainer<kernel_type> trainer;
            trainer.set_kernel(kernel_type(best_gamma));
            trainer.set_c_class1(best_c1);
            trainer.set_c_class2(best_c2);

            if (be_verbose)
                std::cout << "Training final classifier with best parameters..." << std::endl;

            df.function = trainer.train(x,y);

            return df;
        }

    // ----------------------------------------------------------------------------------------

        normalized_function<multiclass_linear_decision_function<linear_kernel<matrix<double,0,1>>, unsigned long>>
        auto_train_multiclass_svm_linear_classifier (
            std::vector<matrix<double,0,1>> x,
            std::vector<unsigned long> y,
            const std::chrono::nanoseconds max_runtime,
            bool be_verbose
        )
        {
            const auto labels = select_all_distinct_labels(y);
            for (const auto label : labels)
            {
                const auto num_samples = sum(mat(y) == label);
                DLIB_CASSERT(num_samples >= 3,
                    "You must provide at least 3 examples of each class to this training routine, however, label "
                    << label << " has only " << num_samples << " examples.");
            }
            DLIB_ASSERT(is_learning_problem(x,y) == true);

            randomize_samples(x, y);

            using kernel_type = linear_kernel<matrix<double,0,1>>;
            normalized_function<multiclass_linear_decision_function<kernel_type, unsigned long>> df;
            // let the normalizer learn the mean and standard deviation of the samples
            df.normalizer.train(x);
            for (auto& samp : x)
                samp = df.normalizer(samp);

            auto cross_validation_score = [&](const double c)
            {
                svm_multiclass_linear_trainer<kernel_type, unsigned long> trainer;
                trainer.set_c(c);
                trainer.set_epsilon(0.01);
                trainer.set_max_iterations(100);
                trainer.set_num_threads(std::thread::hardware_concurrency());

                // Finally, perform 3-fold cross validation and then print and return the confusion matrix.
                const auto cm = cross_validate_multiclass_trainer(trainer, x, y, 3);
                const double accuracy = sum(diag(cm)) / sum(cm);
                if (be_verbose)
                {
                    std::cout << "C: " << c << " cross validation accuracy: " << accuracy << '\n';
                    std::cout << cm << std::endl;
                }
                return accuracy;
            };

            if (be_verbose)
                std::cout << "Searching for best Multiclass linear SVM training parameters..." << std::endl;
            const auto result = find_max_global(cross_validation_score, 1e-3, 1000, max_runtime);

            const double best_c = result.x(0);

            if (be_verbose)
            {
                std::cout << " best cross-validation score: " << result.y << std::endl;
                std::cout << " best C: " << best_c << std::endl;
            }

            svm_multiclass_linear_trainer<kernel_type, unsigned long> trainer;
            trainer.set_num_threads(std::thread::hardware_concurrency());
            trainer.set_c(best_c);

            if (be_verbose)
                std::cout << "Training final classifier with best parameters..." << std::endl;

            df.function = trainer.train(x, y);

            return df;
        }

        normalized_function<multiclass_linear_decision_function<linear_kernel<matrix<float,0,1>>, unsigned long>>
        auto_train_multiclass_svm_linear_classifier (
            const std::vector<matrix<float,0,1>>& x,
            std::vector<unsigned long> y,
            const std::chrono::nanoseconds max_runtime,
            bool be_verbose
        )
        {
            std::vector<matrix<double,0,1>> samples;
            for (const auto& samp : x)
                samples.push_back(matrix_cast<double>(samp));

            const auto temp = auto_train_multiclass_svm_linear_classifier(samples, y, max_runtime, be_verbose);
            normalized_function<multiclass_linear_decision_function<linear_kernel<matrix<float,0,1>>, unsigned long>> df;
            df.normalizer.train(x);
            df.function.labels = temp.function.labels;
            df.function.weights = matrix_cast<float>(temp.function.weights);
            df.function.b = matrix_cast<float>(temp.function.b);
            return df;
        }
    }

    #endif // DLIB_AUTO_LEARnING_CPP_