// Copyright (C) 2010 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_ONE_VS_ONE_TRAiNER_Hh_
#define DLIB_ONE_VS_ONE_TRAiNER_Hh_
#include "one_vs_one_trainer_abstract.h"
#include "one_vs_one_decision_function.h"
#include <vector>
#include "../unordered_pair.h"
#include "multiclass_tools.h"
#include <sstream>
#include <iostream>
#include "../any.h"
#include <map>
#include <set>
#include "../threads.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename any_trainer,
typename label_type_ = double
>
class one_vs_one_trainer
{
public:
typedef label_type_ label_type;
typedef typename any_trainer::sample_type sample_type;
typedef typename any_trainer::scalar_type scalar_type;
typedef typename any_trainer::mem_manager_type mem_manager_type;
typedef one_vs_one_decision_function<one_vs_one_trainer> trained_function_type;
one_vs_one_trainer (
) :
verbose(false),
num_threads(4)
{}
void set_trainer (
const any_trainer& trainer
)
{
default_trainer = trainer;
trainers.clear();
}
void set_trainer (
const any_trainer& trainer,
const label_type& l1,
const label_type& l2
)
{
trainers[make_unordered_pair(l1,l2)] = trainer;
}
void be_verbose (
)
{
verbose = true;
}
void be_quiet (
)
{
verbose = false;
}
void set_num_threads (
unsigned long num
)
{
num_threads = num;
}
unsigned long get_num_threads (
) const
{
return num_threads;
}
struct invalid_label : public dlib::error
{
invalid_label(const std::string& msg, const label_type& l1_, const label_type& l2_
) : dlib::error(msg), l1(l1_), l2(l2_) {};
virtual ~invalid_label(
) throw() {}
label_type l1, l2;
};
trained_function_type train (
const std::vector<sample_type>& all_samples,
const std::vector<label_type>& all_labels
) const
{
// make sure requires clause is not broken
DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
"\t trained_function_type one_vs_one_trainer::train(all_samples,all_labels)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t all_samples.size(): " << all_samples.size()
<< "\n\t all_labels.size(): " << all_labels.size()
);
const std::vector<label_type> distinct_labels = select_all_distinct_labels(all_labels);
// fill pairs with all the pairs of labels.
std::vector<unordered_pair<label_type> > pairs;
for (unsigned long i = 0; i < distinct_labels.size(); ++i)
{
for (unsigned long j = i+1; j < distinct_labels.size(); ++j)
{
pairs.push_back(unordered_pair<label_type>(distinct_labels[i], distinct_labels[j]));
// make sure we have a trainer for this pair
const typename binary_function_table::const_iterator itr = trainers.find(pairs.back());
if (itr == trainers.end() && default_trainer.is_empty())
{
std::ostringstream sout;
sout << "In one_vs_one_trainer, no trainer registered for the ("
<< pairs.back().first << ", " << pairs.back().second << ") label pair.";
throw invalid_label(sout.str(), pairs.back().first, pairs.back().second);
}
}
}
// Now train on all the label pairs.
parallel_for_helper helper(all_samples,all_labels,default_trainer,trainers,verbose,pairs);
parallel_for(num_threads, 0, pairs.size(), helper, 500);
if (helper.error_message.size() != 0)
{
throw dlib::error("binary trainer threw while training one vs. one classifier. Error was: " + helper.error_message);
}
return trained_function_type(helper.dfs);
}
private:
typedef std::map<unordered_pair<label_type>, any_trainer> binary_function_table;
struct parallel_for_helper
{
parallel_for_helper(
const std::vector<sample_type>& all_samples_,
const std::vector<label_type>& all_labels_,
const any_trainer& default_trainer_,
const binary_function_table& trainers_,
const bool verbose_,
const std::vector<unordered_pair<label_type> >& pairs_
) :
all_samples(all_samples_),
all_labels(all_labels_),
default_trainer(default_trainer_),
trainers(trainers_),
verbose(verbose_),
pairs(pairs_)
{}
void operator()(long i) const
{
try
{
std::vector<sample_type> samples;
std::vector<scalar_type> labels;
const unordered_pair<label_type> p = pairs[i];
// pick out the samples corresponding to these two classes
for (unsigned long k = 0; k < all_samples.size(); ++k)
{
if (all_labels[k] == p.first)
{
samples.push_back(all_samples[k]);
labels.push_back(+1);
}
else if (all_labels[k] == p.second)
{
samples.push_back(all_samples[k]);
labels.push_back(-1);
}
}
if (verbose)
{
auto_mutex lock(class_mutex);
std::cout << "Training classifier for " << p.first << " vs. " << p.second << std::endl;
}
any_trainer trainer;
// now train a binary classifier using the samples we selected
{ auto_mutex lock(class_mutex);
const typename binary_function_table::const_iterator itr = trainers.find(p);
if (itr != trainers.end())
trainer = itr->second;
else
trainer = default_trainer;
}
any_decision_function<sample_type,scalar_type> binary_df = trainer.train(samples, labels);
auto_mutex lock(class_mutex);
dfs[p] = binary_df;
}
catch (std::exception& e)
{
auto_mutex lock(class_mutex);
error_message = e.what();
}
}
mutable typename trained_function_type::binary_function_table dfs;
mutex class_mutex;
mutable std::string error_message;
const std::vector<sample_type>& all_samples;
const std::vector<label_type>& all_labels;
const any_trainer& default_trainer;
const binary_function_table& trainers;
const bool verbose;
const std::vector<unordered_pair<label_type> >& pairs;
};
any_trainer default_trainer;
binary_function_table trainers;
bool verbose;
unsigned long num_threads;
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_ONE_VS_ONE_TRAiNER_Hh_