// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_STRUCTURAL_SVM_GRAPH_LAbELING_PROBLEM_Hh_
#define DLIB_STRUCTURAL_SVM_GRAPH_LAbELING_PROBLEM_Hh_
#include "structural_svm_graph_labeling_problem_abstract.h"
#include "../graph_cuts.h"
#include "../matrix.h"
#include "../array.h"
#include <vector>
#include <iterator>
#include "structural_svm_problem_threaded.h"
#include "../graph.h"
#include "sparse_vector.h"
#include <sstream>
// ----------------------------------------------------------------------------------------
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename graph_type
>
bool is_graph_labeling_problem (
const dlib::array<graph_type>& samples,
const std::vector<std::vector<bool> >& labels,
std::string& reason_for_failure
)
{
typedef typename graph_type::type node_vector_type;
typedef typename graph_type::edge_type edge_vector_type;
// The graph must use all dense vectors or all sparse vectors. It can't mix the two types together.
COMPILE_TIME_ASSERT( (is_matrix<node_vector_type>::value && is_matrix<edge_vector_type>::value) ||
(!is_matrix<node_vector_type>::value && !is_matrix<edge_vector_type>::value));
std::ostringstream sout;
reason_for_failure.clear();
if (!is_learning_problem(samples, labels))
{
reason_for_failure = "is_learning_problem(samples, labels) returned false.";
return false;
}
const bool ismat = is_matrix<typename graph_type::type>::value;
// these are -1 until assigned with a value
long node_dims = -1;
long edge_dims = -1;
for (unsigned long i = 0; i < samples.size(); ++i)
{
if (samples[i].number_of_nodes() != labels[i].size())
{
sout << "samples["<<i<<"].number_of_nodes() doesn't match labels["<<i<<"].size().";
reason_for_failure = sout.str();
return false;
}
if (graph_contains_length_one_cycle(samples[i]))
{
sout << "graph_contains_length_one_cycle(samples["<<i<<"]) returned true.";
reason_for_failure = sout.str();
return false;
}
for (unsigned long j = 0; j < samples[i].number_of_nodes(); ++j)
{
if (ismat && samples[i].node(j).data.size() == 0)
{
sout << "A graph contains an empty vector at node: samples["<<i<<"].node("<<j<<").data.";
reason_for_failure = sout.str();
return false;
}
if (ismat && node_dims == -1)
node_dims = samples[i].node(j).data.size();
// all nodes must have vectors of the same size.
if (ismat && (long)samples[i].node(j).data.size() != node_dims)
{
sout << "Not all node vectors in samples["<<i<<"] are the same dimension.";
reason_for_failure = sout.str();
return false;
}
for (unsigned long n = 0; n < samples[i].node(j).number_of_neighbors(); ++n)
{
if (ismat && samples[i].node(j).edge(n).size() == 0)
{
sout << "A graph contains an empty vector at edge: samples["<<i<<"].node("<<j<<").edge("<<n<<").";
reason_for_failure = sout.str();
return false;
}
if (min(samples[i].node(j).edge(n)) < 0)
{
sout << "A graph contains negative values on an edge vector at: samples["<<i<<"].node("<<j<<").edge("<<n<<").";
reason_for_failure = sout.str();
return false;
}
if (ismat && edge_dims == -1)
edge_dims = samples[i].node(j).edge(n).size();
// all edges must have vectors of the same size.
if (ismat && (long)samples[i].node(j).edge(n).size() != edge_dims)
{
sout << "Not all edge vectors in samples["<<i<<"] are the same dimension.";
reason_for_failure = sout.str();
return false;
}
}
}
}
return true;
}
template <
typename graph_type
>
bool is_graph_labeling_problem (
const dlib::array<graph_type>& samples,
const std::vector<std::vector<bool> >& labels
)
{
std::string reason_for_failure;
return is_graph_labeling_problem(samples, labels, reason_for_failure);
}
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U
>
bool sizes_match (
const std::vector<std::vector<T> >& lhs,
const std::vector<std::vector<U> >& rhs
)
{
if (lhs.size() != rhs.size())
return false;
for (unsigned long i = 0; i < lhs.size(); ++i)
{
if (lhs[i].size() != rhs[i].size())
return false;
}
return true;
}
// ----------------------------------------------------------------------------------------
inline bool all_values_are_nonnegative (
const std::vector<std::vector<double> >& x
)
{
for (unsigned long i = 0; i < x.size(); ++i)
{
for (unsigned long j = 0; j < x[i].size(); ++j)
{
if (x[i][j] < 0)
return false;
}
}
return true;
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
namespace impl
{
template <
typename T,
typename enable = void
>
struct fvect
{
// In this case type should be some sparse vector type
typedef typename T::type type;
};
template < typename T >
struct fvect<T, typename enable_if<is_matrix<typename T::type> >::type>
{
// The point of this stuff is to create the proper matrix
// type to represent the concatenation of an edge vector
// with an node vector.
typedef typename T::type node_mat;
typedef typename T::edge_type edge_mat;
const static long NRd = node_mat::NR;
const static long NRe = edge_mat::NR;
const static long NR = ((NRd!=0) && (NRe!=0)) ? (NRd+NRe) : 0;
typedef typename node_mat::value_type value_type;
typedef matrix<value_type,NR,1, typename node_mat::mem_manager_type, typename node_mat::layout_type> type;
};
}
// ----------------------------------------------------------------------------------------
template <
typename graph_type
>
class structural_svm_graph_labeling_problem : noncopyable,
public structural_svm_problem_threaded<matrix<double,0,1>,
typename dlib::impl::fvect<graph_type>::type >
{
public:
typedef matrix<double,0,1> matrix_type;
typedef typename dlib::impl::fvect<graph_type>::type feature_vector_type;
typedef graph_type sample_type;
typedef std::vector<bool> label_type;
structural_svm_graph_labeling_problem(
const dlib::array<sample_type>& samples_,
const std::vector<label_type>& labels_,
const std::vector<std::vector<double> >& losses_,
unsigned long num_threads = 2
) :
structural_svm_problem_threaded<matrix_type,feature_vector_type>(num_threads),
samples(samples_),
labels(labels_),
losses(losses_)
{
// make sure requires clause is not broken
#ifdef ENABLE_ASSERTS
std::string reason_for_failure;
DLIB_ASSERT(is_graph_labeling_problem(samples, labels, reason_for_failure) == true ,
"\t structural_svm_graph_labeling_problem::structural_svm_graph_labeling_problem()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t reason_for_failure: " << reason_for_failure
<< "\n\t samples.size(): " << samples.size()
<< "\n\t labels.size(): " << labels.size()
<< "\n\t this: " << this );
DLIB_ASSERT((losses.size() == 0 || sizes_match(labels, losses) == true) &&
all_values_are_nonnegative(losses) == true,
"\t structural_svm_graph_labeling_problem::structural_svm_graph_labeling_problem()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t labels.size(): " << labels.size()
<< "\n\t losses.size(): " << losses.size()
<< "\n\t sizes_match(labels,losses): " << sizes_match(labels,losses)
<< "\n\t all_values_are_nonnegative(losses): " << all_values_are_nonnegative(losses)
<< "\n\t this: " << this );
#endif
loss_pos = 1.0;
loss_neg = 1.0;
// figure out how many dimensions are in the node and edge vectors.
node_dims = 0;
edge_dims = 0;
for (unsigned long i = 0; i < samples.size(); ++i)
{
for (unsigned long j = 0; j < samples[i].number_of_nodes(); ++j)
{
node_dims = std::max(node_dims,(long)max_index_plus_one(samples[i].node(j).data));
for (unsigned long n = 0; n < samples[i].node(j).number_of_neighbors(); ++n)
{
edge_dims = std::max(edge_dims, (long)max_index_plus_one(samples[i].node(j).edge(n)));
}
}
}
}
const std::vector<std::vector<double> >& get_losses (
) const { return losses; }
long get_num_edge_weights (
) const
{
return edge_dims;
}
void set_loss_on_positive_class (
double loss
)
{
// make sure requires clause is not broken
DLIB_ASSERT(loss >= 0 && get_losses().size() == 0,
"\t void structural_svm_graph_labeling_problem::set_loss_on_positive_class()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t loss: " << loss
<< "\n\t this: " << this );
loss_pos = loss;
}
void set_loss_on_negative_class (
double loss
)
{
// make sure requires clause is not broken
DLIB_ASSERT(loss >= 0 && get_losses().size() == 0,
"\t void structural_svm_graph_labeling_problem::set_loss_on_negative_class()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t loss: " << loss
<< "\n\t this: " << this );
loss_neg = loss;
}
double get_loss_on_negative_class (
) const
{
// make sure requires clause is not broken
DLIB_ASSERT(get_losses().size() == 0,
"\t double structural_svm_graph_labeling_problem::get_loss_on_negative_class()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t this: " << this );
return loss_neg;
}
double get_loss_on_positive_class (
) const
{
// make sure requires clause is not broken
DLIB_ASSERT(get_losses().size() == 0,
"\t double structural_svm_graph_labeling_problem::get_loss_on_positive_class()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t this: " << this );
return loss_pos;
}
private:
virtual long get_num_dimensions (
) const
{
// The psi/w vector will begin with all the edge dims and then follow with the node dims.
return edge_dims + node_dims;
}
virtual long get_num_samples (
) const
{
return samples.size();
}
template <typename psi_type>
typename enable_if<is_matrix<psi_type> >::type get_joint_feature_vector (
const sample_type& sample,
const label_type& label,
psi_type& psi
) const
{
psi.set_size(get_num_dimensions());
psi = 0;
for (unsigned long i = 0; i < sample.number_of_nodes(); ++i)
{
// accumulate the node vectors
if (label[i] == true)
set_rowm(psi, range(edge_dims, psi.size()-1)) += sample.node(i).data;
for (unsigned long n = 0; n < sample.node(i).number_of_neighbors(); ++n)
{
const unsigned long j = sample.node(i).neighbor(n).index();
// Don't double count edges. Also only include the vector if
// the labels disagree.
if (i < j && label[i] != label[j])
{
set_rowm(psi, range(0, edge_dims-1)) -= sample.node(i).edge(n);
}
}
}
}
template <typename T>
void add_to_sparse_vect (
T& psi,
const T& vect,
unsigned long offset
) const
{
for (typename T::const_iterator i = vect.begin(); i != vect.end(); ++i)
{
psi.insert(psi.end(), std::make_pair(i->first+offset, i->second));
}
}
template <typename T>
void subtract_from_sparse_vect (
T& psi,
const T& vect
) const
{
for (typename T::const_iterator i = vect.begin(); i != vect.end(); ++i)
{
psi.insert(psi.end(), std::make_pair(i->first, -i->second));
}
}
template <typename psi_type>
typename disable_if<is_matrix<psi_type> >::type get_joint_feature_vector (
const sample_type& sample,
const label_type& label,
psi_type& psi
) const
{
psi.clear();
for (unsigned long i = 0; i < sample.number_of_nodes(); ++i)
{
// accumulate the node vectors
if (label[i] == true)
add_to_sparse_vect(psi, sample.node(i).data, edge_dims);
for (unsigned long n = 0; n < sample.node(i).number_of_neighbors(); ++n)
{
const unsigned long j = sample.node(i).neighbor(n).index();
// Don't double count edges. Also only include the vector if
// the labels disagree.
if (i < j && label[i] != label[j])
{
subtract_from_sparse_vect(psi, sample.node(i).edge(n));
}
}
}
}
virtual void get_truth_joint_feature_vector (
long idx,
feature_vector_type& psi
) const
{
get_joint_feature_vector(samples[idx], labels[idx], psi);
}
virtual void separation_oracle (
const long idx,
const matrix_type& current_solution,
double& loss,
feature_vector_type& psi
) const
{
const sample_type& samp = samples[idx];
// setup the potts graph based on samples[idx] and current_solution.
graph<double,double>::kernel_1a g;
copy_graph_structure(samp, g);
for (unsigned long i = 0; i < g.number_of_nodes(); ++i)
{
g.node(i).data = dot(rowm(current_solution,range(edge_dims,current_solution.size()-1)),
samp.node(i).data);
// Include a loss augmentation so that we will get the proper loss augmented
// max when we use find_max_factor_graph_potts() below.
if (labels[idx][i])
g.node(i).data -= get_loss_for_sample(idx,i,!labels[idx][i]);
else
g.node(i).data += get_loss_for_sample(idx,i,!labels[idx][i]);
for (unsigned long n = 0; n < g.node(i).number_of_neighbors(); ++n)
{
const unsigned long j = g.node(i).neighbor(n).index();
// Don't compute an edge weight more than once.
if (i < j)
{
g.node(i).edge(n) = dot(rowm(current_solution,range(0,edge_dims-1)),
samp.node(i).edge(n));
}
}
}
std::vector<node_label> labeling;
find_max_factor_graph_potts(g, labeling);
std::vector<bool> bool_labeling;
bool_labeling.reserve(labeling.size());
// figure out the loss
loss = 0;
for (unsigned long i = 0; i < labeling.size(); ++i)
{
const bool predicted_label = (labeling[i]!= 0);
bool_labeling.push_back(predicted_label);
loss += get_loss_for_sample(idx, i, predicted_label);
}
// compute psi
get_joint_feature_vector(samp, bool_labeling, psi);
}
double get_loss_for_sample (
long sample_idx,
long node_idx,
bool predicted_label
) const
/*!
requires
- 0 <= sample_idx < labels.size()
- 0 <= node_idx < labels[sample_idx].size()
ensures
- returns the loss incurred for predicting that the node
samples[sample_idx].node(node_idx) has a label of predicted_label.
!*/
{
const bool true_label = labels[sample_idx][node_idx];
if (true_label != predicted_label)
{
if (losses.size() != 0)
return losses[sample_idx][node_idx];
else if (true_label == true)
return loss_pos;
else
return loss_neg;
}
else
{
// no loss for making the correct prediction.
return 0;
}
}
const dlib::array<sample_type>& samples;
const std::vector<label_type>& labels;
const std::vector<std::vector<double> >& losses;
long node_dims;
long edge_dims;
double loss_pos;
double loss_neg;
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STRUCTURAL_SVM_GRAPH_LAbELING_PROBLEM_Hh_