dlib C++ Library - pegasos_abstract.h

// Copyright (C) 2009 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_PEGASoS_ABSTRACT_
#ifdef DLIB_PEGASoS_ABSTRACT_
#include <cmath>
#include "../algs.h"
#include "function_abstract.h"
#include "kernel_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
 template <
 typename kern_type
 >
 class svm_pegasos
 {
 /*!
 REQUIREMENTS ON kern_type
 is a kernel function object as defined in dlib/svm/kernel_abstract.h 
 WHAT THIS OBJECT REPRESENTS
 This object implements an online algorithm for training a support 
 vector machine for solving binary classification problems. 
 The implementation of the Pegasos algorithm used by this object is based
 on the following excellent paper:
 Pegasos: Primal estimated sub-gradient solver for SVM (2007)
 by Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro 
 In ICML 
 This SVM training algorithm has two interesting properties. First, the 
 pegasos algorithm itself converges to the solution in an amount of time
 unrelated to the size of the training set (in addition to being quite fast
 to begin with). This makes it an appropriate algorithm for learning from
 very large datasets. Second, this object uses the dlib::kcentroid object 
 to maintain a sparse approximation of the learned decision function. 
 This means that the number of support vectors in the resulting decision 
 function is also unrelated to the size of the dataset (in normal SVM
 training algorithms, the number of support vectors grows approximately 
 linearly with the size of the training set). 
 !*/
 public:
 typedef kern_type kernel_type;
 typedef typename kernel_type::scalar_type scalar_type;
 typedef typename kernel_type::sample_type sample_type;
 typedef typename kernel_type::mem_manager_type mem_manager_type;
 typedef decision_function<kernel_type> trained_function_type;
 template <typename K_>
 struct rebind {
 typedef svm_pegasos<K_> other;
 };
 svm_pegasos (
 );
 /*!
 ensures
 - this object is properly initialized 
 - #get_lambda_class1() == 0.0001
 - #get_lambda_class2() == 0.0001
 - #get_tolerance() == 0.01
 - #get_train_count() == 0
 - #get_max_num_sv() == 40
 !*/
 svm_pegasos (
 const kernel_type& kernel_, 
 const scalar_type& lambda_,
 const scalar_type& tolerance_,
 unsigned long max_num_sv
 );
 /*!
 requires
 - lambda_ > 0
 - tolerance_ > 0
 - max_num_sv > 0
 ensures
 - this object is properly initialized 
 - #get_lambda_class1() == lambda_ 
 - #get_lambda_class2() == lambda_ 
 - #get_tolerance() == tolerance_
 - #get_kernel() == kernel_
 - #get_train_count() == 0
 - #get_max_num_sv() == max_num_sv
 !*/
 void clear (
 );
 /*!
 ensures
 - #get_train_count() == 0
 - clears out any memory of previous calls to train()
 - doesn't change any of the algorithm parameters. I.e.
 - #get_lambda_class1() == get_lambda_class1()
 - #get_lambda_class2() == get_lambda_class2()
 - #get_tolerance() == get_tolerance()
 - #get_kernel() == get_kernel()
 - #get_max_num_sv() == get_max_num_sv()
 !*/
 const scalar_type get_lambda_class1 (
 ) const;
 /*!
 ensures
 - returns the SVM regularization term for the +1 class. It is the 
 parameter that determines the trade off between trying to fit the 
 +1 training data exactly or allowing more errors but hopefully 
 improving the generalization ability of the resulting classifier. 
 Smaller values encourage exact fitting while larger values may 
 encourage better generalization. It is also worth noting that the 
 number of iterations it takes for this algorithm to converge is 
 proportional to 1/lambda. So smaller values of this term cause 
 the running time of this algorithm to increase. For more 
 information you should consult the paper referenced above.
 !*/
 const scalar_type get_lambda_class2 (
 ) const;
 /*!
 ensures
 - returns the SVM regularization term for the -1 class. It has
 the same properties as the get_lambda_class1() parameter except that
 it applies to the -1 class.
 !*/
 const scalar_type get_tolerance (
 ) const;
 /*!
 ensures
 - returns the tolerance used by the internal kcentroid object to 
 represent the learned decision function. Smaller values of this 
 tolerance will result in a more accurate representation of the 
 decision function but will use more support vectors (up to
 a max of get_max_num_sv()). 
 !*/
 unsigned long get_max_num_sv (
 ) const;
 /*!
 ensures
 - returns the maximum number of support vectors this object is
 allowed to use.
 !*/
 const kernel_type get_kernel (
 ) const;
 /*!
 ensures
 - returns the kernel used by this object
 !*/
 void set_kernel (
 kernel_type k
 );
 /*!
 ensures
 - #get_kernel() == k
 - #get_train_count() == 0
 (i.e. clears any memory of previous training)
 !*/
 void set_tolerance (
 double tol
 );
 /*!
 requires
 - tol > 0
 ensures
 - #get_tolerance() == tol
 - #get_train_count() == 0
 (i.e. clears any memory of previous training)
 !*/
 void set_max_num_sv (
 unsigned long max_num_sv
 );
 /*!
 requires
 - max_num_sv > 0
 ensures
 - #get_max_num_sv() == max_num_sv 
 - #get_train_count() == 0
 (i.e. clears any memory of previous training)
 !*/
 void set_lambda (
 scalar_type lambda_
 );
 /*!
 requires
 - lambda_ > 0
 ensures
 - #get_lambda_class1() == lambda_
 - #get_lambda_class2() == lambda_
 - #get_train_count() == 0
 (i.e. clears any memory of previous training)
 !*/
 void set_lambda_class1 (
 scalar_type lambda_
 );
 /*!
 requires
 - lambda_ > 0
 ensures
 - #get_lambda_class1() == lambda_ 
 #get_train_count() == 0
 (i.e. clears any memory of previous training)
 !*/
 void set_lambda_class2 (
 scalar_type lambda_
 );
 /*!
 requires
 - lambda_ > 0
 ensures
 - #get_lambda_class2() == lambda_ 
 #get_train_count() == 0
 (i.e. clears any memory of previous training)
 !*/
 unsigned long get_train_count (
 ) const;
 /*!
 ensures
 - returns how many times this->train() has been called
 since this object was constructed or last cleared. 
 !*/
 scalar_type train (
 const sample_type& x,
 const scalar_type& y
 );
 /*!
 requires
 - y == 1 || y == -1
 ensures
 - trains this svm using the given sample x and label y
 - #get_train_count() == get_train_count() + 1
 - returns the current learning rate
 (i.e. 1/(get_train_count()*min(get_lambda_class1(),get_lambda_class2())) )
 !*/
 scalar_type operator() (
 const sample_type& x
 ) const;
 /*!
 ensures
 - classifies the given x sample using the decision function
 this object has learned so far. 
 - if (x is a sample predicted have +1 label) then
 - returns a number >= 0 
 - else
 - returns a number < 0
 !*/
 const decision_function<kernel_type> get_decision_function (
 ) const;
 /*!
 ensures
 - returns a decision function F that represents the function learned 
 by this object so far. I.e. it is the case that:
 - for all x: F(x) == (*this)(x)
 !*/
 void swap (
 svm_pegasos& item
 );
 /*!
 ensures
 - swaps *this and item
 !*/
 }; 
// ----------------------------------------------------------------------------------------
 template <
 typename kern_type 
 >
 void swap(
 svm_pegasos<kern_type>& a, 
 svm_pegasos<kern_type>& b
 ) { a.swap(b); }
 /*!
 provides a global swap function
 !*/
 template <
 typename kern_type
 >
 void serialize (
 const svm_pegasos<kern_type>& item,
 std::ostream& out
 );
 /*!
 provides serialization support for svm_pegasos objects
 !*/
 template <
 typename kern_type 
 >
 void deserialize (
 svm_pegasos<kern_type>& item,
 std::istream& in 
 );
 /*!
 provides serialization support for svm_pegasos objects
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename T,
 typename U
 >
 void replicate_settings (
 const svm_pegasos<T>& source,
 svm_pegasos<U>& dest
 );
 /*!
 ensures
 - copies all the parameters from the source trainer to the dest trainer.
 - #dest.get_tolerance() == source.get_tolerance()
 - #dest.get_lambda_class1() == source.get_lambda_class1()
 - #dest.get_lambda_class2() == source.get_lambda_class2()
 - #dest.get_max_num_sv() == source.get_max_num_sv()
 !*/
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
 template <
 typename trainer_type
 >
 class batch_trainer 
 {
 /*!
 REQUIREMENTS ON trainer_type
 - trainer_type == some kind of online trainer object (e.g. svm_pegasos)
 replicate_settings() must also be defined for the type.
 WHAT THIS OBJECT REPRESENTS
 This is a trainer object that is meant to wrap online trainer objects 
 that create decision_functions. It turns an online learning algorithm 
 such as svm_pegasos into a batch learning object. This allows you to 
 use objects like svm_pegasos with functions (e.g. cross_validate_trainer) 
 that expect batch mode training objects.
 !*/
 public:
 typedef typename trainer_type::kernel_type kernel_type;
 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 typename trainer_type::trained_function_type trained_function_type;
 batch_trainer (
 );
 /*!
 ensures
 - This object is in an uninitialized state. You must
 construct a real one with the other constructor and assign it
 to this instance before you use this object.
 !*/
 batch_trainer (
 const trainer_type& online_trainer, 
 const scalar_type min_learning_rate_,
 bool verbose_,
 bool use_cache_,
 long cache_size_ = 100
 );
 /*!
 requires
 - min_learning_rate_ > 0
 - cache_size_ > 0
 ensures
 - returns a batch trainer object that uses the given online_trainer object
 to train a decision function.
 - #get_min_learning_rate() == min_learning_rate_
 - if (verbose_ == true) then
 - this object will output status messages to standard out while
 training is under way.
 - if (use_cache_ == true) then
 - this object will cache up to cache_size_ columns of the kernel 
 matrix during the training process.
 !*/
 const scalar_type get_min_learning_rate (
 ) const;
 /*!
 ensures
 - returns the min learning rate that the online trainer must reach
 before this object considers training to be complete.
 !*/
 template <
 typename in_sample_vector_type,
 typename in_scalar_vector_type
 >
 const decision_function<kernel_type> train (
 const in_sample_vector_type& x,
 const in_scalar_vector_type& y
 ) const;
 /*!
 ensures
 - trains and returns a decision_function using the trainer that was 
 supplied to this object's constructor.
 - training continues until the online training object indicates that
 its learning rate has dropped below get_min_learning_rate().
 throws
 - std::bad_alloc
 - any exceptions thrown by the trainer_type object
 !*/
 }; 
// ----------------------------------------------------------------------------------------
 template <
 typename trainer_type
 >
 const batch_trainer<trainer_type> batch (
 const trainer_type& trainer,
 const typename trainer_type::scalar_type min_learning_rate = 0.1
 ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false, false); }
 /*!
 requires
 - min_learning_rate > 0
 - trainer_type == some kind of online trainer object that creates decision_function
 objects (e.g. svm_pegasos). replicate_settings() must also be defined for the type.
 ensures
 - returns a batch_trainer object that has been instantiated with the 
 given arguments.
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename trainer_type
 >
 const batch_trainer<trainer_type> verbose_batch (
 const trainer_type& trainer,
 const typename trainer_type::scalar_type min_learning_rate = 0.1
 ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true, false); }
 /*!
 requires
 - min_learning_rate > 0
 - trainer_type == some kind of online trainer object that creates decision_function
 objects (e.g. svm_pegasos). replicate_settings() must also be defined for the type.
 ensures
 - returns a batch_trainer object that has been instantiated with the 
 given arguments (and is verbose).
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename trainer_type
 >
 const batch_trainer<trainer_type> batch_cached (
 const trainer_type& trainer,
 const typename trainer_type::scalar_type min_learning_rate = 0.1,
 long cache_size = 100
 ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false, true, cache_size); }
 /*!
 requires
 - min_learning_rate > 0
 - cache_size > 0
 - trainer_type == some kind of online trainer object that creates decision_function
 objects (e.g. svm_pegasos). replicate_settings() must also be defined for the type.
 ensures
 - returns a batch_trainer object that has been instantiated with the 
 given arguments (uses a kernel cache).
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename trainer_type
 >
 const batch_trainer<trainer_type> verbose_batch_cached (
 const trainer_type& trainer,
 const typename trainer_type::scalar_type min_learning_rate = 0.1,
 long cache_size = 100
 ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true, true, cache_size); }
 /*!
 requires
 - min_learning_rate > 0
 - cache_size > 0
 - trainer_type == some kind of online trainer object that creates decision_function
 objects (e.g. svm_pegasos). replicate_settings() must also be defined for the type.
 ensures
 - returns a batch_trainer object that has been instantiated with the 
 given arguments (is verbose and uses a kernel cache).
 !*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_PEGASoS_ABSTRACT_