dlib C++ Library - elastic_net.cpp

// Copyright (C) 2016 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <dlib/optimization/elastic_net.h>
#include "tester.h"
#include <dlib/svm.h>
#include <dlib/rand.h>
#include <dlib/string.h>
#include <vector>
#include <sstream>
#include <ctime>
namespace 
{
 using namespace test;
 using namespace dlib;
 using namespace std;
 dlib::logger dlog("test.elastic_net");
// ----------------------------------------------------------------------------------------
 matrix<double,0,1> basic_elastic_net(
 const matrix<double>& X,
 const matrix<double,0,1>& Y,
 double ridge_lambda,
 double lasso_budget,
 double eps
 )
 {
 DLIB_CASSERT(X.nc() == Y.nr(),"");
 typedef matrix<double,0,1> sample_type;
 typedef linear_kernel<sample_type> kernel_type;
 svm_c_linear_dcd_trainer<kernel_type> trainer;
 trainer.solve_svm_l2_problem(true);
 const double C = 1/(2*ridge_lambda);
 trainer.set_c(C);
 trainer.set_epsilon(eps);
 trainer.enable_shrinking(true);
 trainer.include_bias(false);
 std::vector<sample_type> samples;
 std::vector<double> labels;
 for (long r = 0; r < X.nr(); ++r)
 {
 sample_type temp = trans(rowm(X,r));
 const double xmul = (1/lasso_budget);
 samples.push_back(temp - xmul*Y);
 labels.push_back(+1);
 samples.push_back(temp + xmul*Y);
 labels.push_back(-1);
 }
 svm_c_linear_dcd_trainer<kernel_type>::optimizer_state state;
 auto df = trainer.train(samples, labels, state);
 auto&& alpha = state.get_alpha();
 matrix<double,0,1> betas(alpha.size()/2);
 for (long i = 0; i < betas.size(); ++i)
 betas(i) = lasso_budget*(alpha[2*i] - alpha[2*i+1]);
 betas /= sum(mat(alpha));
 return betas;
 }
// ----------------------------------------------------------------------------------------
 class test_elastic_net : public tester
 {
 public:
 test_elastic_net (
 ) :
 tester (
 "test_elastic_net", 
 "Run tests on the elastic_net object.", 
 0 
 )
 {
 }
 void perform_test (
 )
 {
 matrix<double> w = {1,2,0,4, 0,0,0,0,0, 6, 7,8,0, 9, 0};
 matrix<double> X = randm(w.size(),1000);
 matrix<double> Y = trans(X)*w;
 Y += 0.1*(randm(Y.nr(), Y.nc())-0.5);
 double ridge_lambda = 0.1;
 double lasso_budget = sum(abs(w));
 double eps = 0.0000001;
 dlib::elastic_net solver(X*trans(X),X*Y);
 solver.set_epsilon(eps);
 matrix<double,0,1> results;
 matrix<double,0,1> results2;
 for (double s = 1.2; s > 0.10; s *= 0.9)
 {
 print_spinner();
 dlog << LINFO << "s: "<< s;
 // make sure the two solvers agree. 
 results = basic_elastic_net(X, Y, ridge_lambda, lasso_budget*s, eps);
 results2 = solver(ridge_lambda, lasso_budget*s);
 dlog << LINFO << "error: "<< max(abs(results - results2));
 DLIB_TEST(max(abs(results - results2)) < 1e-3);
 }
 }
 } a;
// ----------------------------------------------------------------------------------------
}