// Copyright (C) 2008 Davis E. King (davis@dlib.net) // License: Boost Software License See LICENSE.txt for the full license. #ifndef DLIB_SVm_THREADED_ #define DLIB_SVm_THREADED_ #include <cmath> #include <iostream> #include <limits> #include <sstream> #include <vector> #include "svm_threaded_abstract.h" #include "svm.h" #include "../matrix.h" #include "../algs.h" #include "../serialize.h" #include "function.h" #include "kernel.h" #include "../threads.h" #include "../pipe.h" namespace dlib { // ---------------------------------------------------------------------------------------- namespace cvtti_helpers { template <typename trainer_type, typename in_sample_vector_type> struct job { typedef typename trainer_type::scalar_type scalar_type; typedef typename trainer_type::sample_type sample_type; typedef typename trainer_type::mem_manager_type mem_manager_type; typedef matrix<sample_type,0,1,mem_manager_type> sample_vector_type; typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type; job() : x(0) {} trainer_type trainer; matrix<long,0,1> x_test, x_train; scalar_vector_type y_test, y_train; const in_sample_vector_type* x; }; struct task { template < typename trainer_type, typename mem_manager_type, typename in_sample_vector_type > void operator()( job<trainer_type,in_sample_vector_type>& j, matrix<double,1,2,mem_manager_type>& result ) { try { result = test_binary_decision_function(j.trainer.train(rowm(*j.x,j.x_train), j.y_train), rowm(*j.x,j.x_test), j.y_test); // Do this just to make j release it's memory since people might run threaded cross validation // on very large datasets. Every bit of freed memory helps out. j = job<trainer_type,in_sample_vector_type>(); } catch (invalid_nu_error&) { // If this is a svm_nu_trainer then we might get this exception if the nu is // invalid. In this case just return a cross validation score of 0. result = 0; } catch (std::bad_alloc&) { std::cerr << "\nstd::bad_alloc thrown while running cross_validate_trainer_threaded(). Not enough memory.\n" << std::endl; throw; } } }; } template < typename trainer_type, typename in_sample_vector_type, typename in_scalar_vector_type > const matrix<double, 1, 2, typename trainer_type::mem_manager_type> cross_validate_trainer_threaded_impl ( const trainer_type& trainer, const in_sample_vector_type& x, const in_scalar_vector_type& y, const long folds, const long num_threads ) { using namespace dlib::cvtti_helpers; typedef typename trainer_type::mem_manager_type mem_manager_type; // make sure requires clause is not broken DLIB_ASSERT(is_binary_classification_problem(x,y) == true && 1 < folds && folds <= std::min(sum(y>0),sum(y<0)) && num_threads > 0, "\tmatrix cross_validate_trainer()" << "\n\t invalid inputs were given to this function" << "\n\t std::min(sum(y>0),sum(y<0)): " << std::min(sum(y>0),sum(y<0)) << "\n\t folds: " << folds << "\n\t num_threads: " << num_threads << "\n\t is_binary_classification_problem(x,y): " << ((is_binary_classification_problem(x,y))? "true":"false") ); task mytask; thread_pool tp(num_threads); // count the number of positive and negative examples long num_pos = 0; long num_neg = 0; for (long r = 0; r < y.nr(); ++r) { if (y(r) == +1.0) ++num_pos; else ++num_neg; } // figure out how many positive and negative examples we will have in each fold const long num_pos_test_samples = num_pos/folds; const long num_pos_train_samples = num_pos - num_pos_test_samples; const long num_neg_test_samples = num_neg/folds; const long num_neg_train_samples = num_neg - num_neg_test_samples; long pos_idx = 0; long neg_idx = 0; std::vector<future<job<trainer_type,in_sample_vector_type> > > jobs(folds); std::vector<future<matrix<double, 1, 2, mem_manager_type> > > results(folds); for (long i = 0; i < folds; ++i) { job<trainer_type,in_sample_vector_type>& j = jobs[i].get(); j.x = &x; j.x_test.set_size (num_pos_test_samples + num_neg_test_samples); j.y_test.set_size (num_pos_test_samples + num_neg_test_samples); j.x_train.set_size(num_pos_train_samples + num_neg_train_samples); j.y_train.set_size(num_pos_train_samples + num_neg_train_samples); j.trainer = trainer; long cur = 0; // load up our positive test samples while (cur < num_pos_test_samples) { if (y(pos_idx) == +1.0) { j.x_test(cur) = pos_idx; j.y_test(cur) = +1.0; ++cur; } pos_idx = (pos_idx+1)%x.nr(); } // load up our negative test samples while (cur < j.x_test.nr()) { if (y(neg_idx) == -1.0) { j.x_test(cur) = neg_idx; j.y_test(cur) = -1.0; ++cur; } neg_idx = (neg_idx+1)%x.nr(); } // load the training data from the data following whatever we loaded // as the testing data long train_pos_idx = pos_idx; long train_neg_idx = neg_idx; cur = 0; // load up our positive train samples while (cur < num_pos_train_samples) { if (y(train_pos_idx) == +1.0) { j.x_train(cur) = train_pos_idx; j.y_train(cur) = +1.0; ++cur; } train_pos_idx = (train_pos_idx+1)%x.nr(); } // load up our negative train samples while (cur < j.x_train.nr()) { if (y(train_neg_idx) == -1.0) { j.x_train(cur) = train_neg_idx; j.y_train(cur) = -1.0; ++cur; } train_neg_idx = (train_neg_idx+1)%x.nr(); } // finally spawn a task to process this job tp.add_task(mytask, jobs[i], results[i]); } // for (long i = 0; i < folds; ++i) matrix<double, 1, 2, mem_manager_type> res; set_all_elements(res,0); // now compute the total results for (long i = 0; i < folds; ++i) { res += results[i].get(); } return res/(double)folds; } template < typename trainer_type, typename in_sample_vector_type, typename in_scalar_vector_type > const matrix<double, 1, 2, typename trainer_type::mem_manager_type> cross_validate_trainer_threaded ( const trainer_type& trainer, const in_sample_vector_type& x, const in_scalar_vector_type& y, const long folds, const long num_threads ) { return cross_validate_trainer_threaded_impl(trainer, mat(x), mat(y), folds, num_threads); } // ---------------------------------------------------------------------------------------- } #endif // DLIB_SVm_THREADED_