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