// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_STRUCTURAL_GRAPH_LABELING_tRAINER_ABSTRACT_Hh_
#ifdef DLIB_STRUCTURAL_GRAPH_LABELING_tRAINER_ABSTRACT_Hh_
#include "../algs.h"
#include "../optimization.h"
#include "structural_svm_graph_labeling_problem_abstract.h"
#include "../graph_cuts/graph_labeler_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename vector_type
>
class structural_graph_labeling_trainer
{
/*!
REQUIREMENTS ON vector_type
- vector_type is a dlib::matrix capable of representing column
vectors or it is a sparse vector type as defined in dlib/svm/sparse_vector_abstract.h.
WHAT THIS OBJECT REPRESENTS
This object is a tool for learning to solve a graph labeling problem based
on a training dataset of example labeled graphs. The training procedure
produces a graph_labeler object which can be used to predict the labelings
of new graphs.
Note that this is just a convenience wrapper around the
structural_svm_graph_labeling_problem to make it look
similar to all the other trainers in dlib.
!*/
public:
typedef std::vector<bool> label_type;
typedef graph_labeler<vector_type> trained_function_type;
structural_graph_labeling_trainer (
);
/*!
ensures
- #get_c() == 10
- this object isn't verbose
- #get_epsilon() == 0.1
- #get_num_threads() == 2
- #get_max_cache_size() == 5
- #get_loss_on_positive_class() == 1.0
- #get_loss_on_negative_class() == 1.0
!*/
void set_num_threads (
unsigned long num
);
/*!
ensures
- #get_num_threads() == num
!*/
unsigned long get_num_threads (
) const;
/*!
ensures
- returns the number of threads used during training. You should
usually set this equal to the number of processing cores on your
machine.
!*/
void set_epsilon (
double eps
);
/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/
double get_epsilon (
) const;
/*!
ensures
- returns the error epsilon that determines when training should stop.
Smaller values may result in a more accurate solution but take longer
to train. You can think of this epsilon value as saying "solve the
optimization problem until the average number of labeling mistakes per
example graph is within epsilon of its optimal value".
!*/
void set_max_cache_size (
unsigned long max_size
);
/*!
ensures
- #get_max_cache_size() == max_size
!*/
unsigned long get_max_cache_size (
) const;
/*!
ensures
- During training, this object basically runs the graph_labeler on each
training sample, over and over. To speed this up, it is possible to
cache the results of these invocations. This function returns the number
of cache elements per training sample kept in the cache. Note that a value
of 0 means caching is not used at all.
!*/
void be_verbose (
);
/*!
ensures
- This object will print status messages to standard out so that a
user can observe the progress of the algorithm.
!*/
void be_quiet (
);
/*!
ensures
- this object will not print anything to standard out
!*/
void set_oca (
const oca& item
);
/*!
ensures
- #get_oca() == item
!*/
const oca get_oca (
) const;
/*!
ensures
- returns a copy of the optimizer used to solve the structural SVM problem.
!*/
void set_c (
double C
);
/*!
requires
- C > 0
ensures
- #get_c() = C
!*/
double get_c (
) const;
/*!
ensures
- returns the SVM regularization parameter. It is the parameter
that determines the trade-off between trying to fit the training
data (i.e. minimize the loss) or allowing more errors but hopefully
improving the generalization of the resulting graph_labeler. Larger
values encourage exact fitting while smaller values of C may encourage
better generalization.
!*/
void set_loss_on_positive_class (
double loss
);
/*!
requires
- loss >= 0
ensures
- #get_loss_on_positive_class() == loss
!*/
void set_loss_on_negative_class (
double loss
);
/*!
requires
- loss >= 0
ensures
- #get_loss_on_negative_class() == loss
!*/
double get_loss_on_positive_class (
) const;
/*!
ensures
- returns the loss incurred when a graph node which is supposed to have
a label of true gets misclassified. This value controls how much we care
about correctly classifying nodes which should be labeled as true. Larger
loss values indicate that we care more strongly than smaller values.
!*/
double get_loss_on_negative_class (
) const;
/*!
ensures
- returns the loss incurred when a graph node which is supposed to have
a label of false gets misclassified. This value controls how much we care
about correctly classifying nodes which should be labeled as false. Larger
loss values indicate that we care more strongly than smaller values.
!*/
template <
typename graph_type
>
const graph_labeler<vector_type> train (
const dlib::array<graph_type>& samples,
const std::vector<label_type>& labels
) const;
/*!
requires
- is_graph_labeling_problem(samples,labels) == true
ensures
- Uses the structural_svm_graph_labeling_problem to train a graph_labeler
on the given samples/labels training pairs. The idea is to learn to
predict a label given an input sample.
- The values of get_loss_on_positive_class() and get_loss_on_negative_class()
are used to determine how to value mistakes on each node during training.
- returns a function F with the following properties:
- F(new_sample) == The predicted labels for the nodes in the graph
new_sample.
!*/
template <
typename graph_type
>
const graph_labeler<vector_type> train (
const dlib::array<graph_type>& samples,
const std::vector<label_type>& labels,
const std::vector<std::vector<double> >& losses
) const;
/*!
requires
- is_graph_labeling_problem(samples,labels) == true
- if (losses.size() != 0) then
- sizes_match(labels, losses) == true
- all_values_are_nonnegative(losses) == true
ensures
- Uses the structural_svm_graph_labeling_problem to train a graph_labeler
on the given samples/labels training pairs. The idea is to learn to
predict a label given an input sample.
- returns a function F with the following properties:
- F(new_sample) == The predicted labels for the nodes in the graph
new_sample.
- if (losses.size() == 0) then
- The values of get_loss_on_positive_class() and get_loss_on_negative_class()
are used to determine how to value mistakes on each node during training.
- The losses argument is effectively ignored if its size is zero.
- else
- Each node in the training data has its own loss value defined by the
corresponding entry of losses. In particular, this means that the
node with label labels[i][j] incurs a loss of losses[i][j] if it is
incorrectly labeled.
- The get_loss_on_positive_class() and get_loss_on_negative_class()
parameters are ignored. Only losses is used in this case.
!*/
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STRUCTURAL_GRAPH_LABELING_tRAINER_ABSTRACT_Hh_