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/* */
/* Copyright 2008-2009 by Ullrich Koethe and Rahul Nair */
/* */
/* This file is part of the VIGRA computer vision library. */
/* The VIGRA Website is */
/* http://hci.iwr.uni-heidelberg.de/vigra/ */
/* Please direct questions, bug reports, and contributions to */
/* ullrich.koethe@iwr.uni-heidelberg.de or */
/* vigra@informatik.uni-hamburg.de */
/* */
/* Permission is hereby granted, free of charge, to any person */
/* obtaining a copy of this software and associated documentation */
/* files (the "Software"), to deal in the Software without */
/* restriction, including without limitation the rights to use, */
/* copy, modify, merge, publish, distribute, sublicense, and/or */
/* sell copies of the Software, and to permit persons to whom the */
/* Software is furnished to do so, subject to the following */
/* conditions: */
/* */
/* The above copyright notice and this permission notice shall be */
/* included in all copies or substantial portions of the */
/* Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES */
/* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND */
/* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT */
/* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, */
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/* OTHER DEALINGS IN THE SOFTWARE. */
/* */
/************************************************************************/
#ifndef VIGRA_RF_COMMON_HXX
#define VIGRA_RF_COMMON_HXX
namespace vigra
{
struct ClassificationTag
{};
struct RegressionTag
{};
namespace detail
{
class RF_DEFAULT;
}
inline detail::RF_DEFAULT& rf_default();
namespace detail
{
/* \brief singleton default tag class -
*
* use the rf_default() factory function to use the tag.
* \sa RandomForest<>::learn();
*/
class RF_DEFAULT
{
private:
RF_DEFAULT()
{}
public:
friend RF_DEFAULT& ::vigra::rf_default();
/** ok workaround for automatic choice of the decisiontree
* stackentry.
*/
};
/* \brief chooses between default type and type supplied
*
* This is an internal class and you shouldn't really care about it.
* Just pass on used in RandomForest.learn()
* Usage:
*\code
* // example: use container type supplied by user or ArrayVector if
* // rf_default() was specified as argument;
* template<class Container_t>
* void do_some_foo(Container_t in)
* {
* typedef ArrayVector<int> Default_Container_t;
* Default_Container_t default_value;
* Value_Chooser<Container_t, Default_Container_t>
* choose(in, default_value);
*
* // if the user didn't care and the in was of type
* // RF_DEFAULT then default_value is used.
* do_some_more_foo(choose.value());
* }
* Value_Chooser choose_val<Type, Default_Type>
*\endcode
*/
template<class T, class C>
class Value_Chooser
{
public:
typedef T type;
static T & choose(T & t, C &)
{
return t;
}
};
template<class C>
class Value_Chooser<detail::RF_DEFAULT, C>
{
public:
typedef C type;
static C & choose(detail::RF_DEFAULT &, C & c)
{
return c;
}
};
} //namespace detail
/**\brief factory function to return a RF_DEFAULT tag
* \sa RandomForest<>::learn()
*/
detail::RF_DEFAULT& rf_default()
{
static detail::RF_DEFAULT result;
return result;
}
/** tags used with the RandomForestOptions class
* \sa RF_Traits::Option_t
*/
enum RF_OptionTag { RF_EQUAL,
RF_PROPORTIONAL,
RF_EXTERNAL,
RF_NONE,
RF_FUNCTION,
RF_LOG,
RF_SQRT,
RF_CONST,
RF_ALL};
/** \addtogroup MachineLearning
**/
//@{
/**\brief Options object for the random forest
*
* usage:
* RandomForestOptions a = RandomForestOptions()
* .param1(value1)
* .param2(value2)
* ...
*
* This class only contains options/parameters that are not problem
* dependent. The ProblemSpec class contains methods to set class weights
* if necessary.
*
* Note that the return value of all methods is *this which makes
* concatenating of options as above possible.
*/
class RandomForestOptions
{
public:
/**\name sampling options*/
/*\{*/
// look at the member access functions for documentation
double training_set_proportion_;
int training_set_size_;
int (*training_set_func_)(int);
RF_OptionTag
training_set_calc_switch_;
bool sample_with_replacement_;
RF_OptionTag
stratification_method_;
/**\name general random forest options
*
* these usually will be used by most split functors and
* stopping predicates
*/
/*\{*/
RF_OptionTag mtry_switch_;
int mtry_;
int (*mtry_func_)(int) ;
bool predict_weighted_;
int tree_count_;
int min_split_node_size_;
bool prepare_online_learning_;
/*\}*/
typedef ArrayVector<double> double_array;
typedef std::map<std::string, double_array> map_type;
int serialized_size() const
{
return 12;
}
bool operator==(RandomForestOptions & rhs) const
{
bool result = true;
#define COMPARE(field) result = result && (this->field == rhs.field);
COMPARE(training_set_proportion_);
COMPARE(training_set_size_);
COMPARE(training_set_calc_switch_);
COMPARE(sample_with_replacement_);
COMPARE(stratification_method_);
COMPARE(mtry_switch_);
COMPARE(mtry_);
COMPARE(tree_count_);
COMPARE(min_split_node_size_);
COMPARE(predict_weighted_);
#undef COMPARE
return result;
}
bool operator!=(RandomForestOptions & rhs_) const
{
return !(*this == rhs_);
}
template<class Iter>
void unserialize(Iter const & begin, Iter const & end)
{
Iter iter = begin;
vigra_precondition(static_cast<int>(end - begin) == serialized_size(),
"RandomForestOptions::unserialize():"
"wrong number of parameters");
#define PULL(item_, type_) item_ = type_(*iter); ++iter;
PULL(training_set_proportion_, double);
PULL(training_set_size_, int);
++iter; //PULL(training_set_func_, double);
PULL(training_set_calc_switch_, (RF_OptionTag)int);
PULL(sample_with_replacement_, 0 != );
PULL(stratification_method_, (RF_OptionTag)int);
PULL(mtry_switch_, (RF_OptionTag)int);
PULL(mtry_, int);
++iter; //PULL(mtry_func_, double);
PULL(tree_count_, int);
PULL(min_split_node_size_, int);
PULL(predict_weighted_, 0 !=);
#undef PULL
}
template<class Iter>
void serialize(Iter const & begin, Iter const & end) const
{
Iter iter = begin;
vigra_precondition(static_cast<int>(end - begin) == serialized_size(),
"RandomForestOptions::serialize():"
"wrong number of parameters");
#define PUSH(item_) *iter = double(item_); ++iter;
PUSH(training_set_proportion_);
PUSH(training_set_size_);
if(training_set_func_ != 0)
{
PUSH(1);
}
else
{
PUSH(0);
}
PUSH(training_set_calc_switch_);
PUSH(sample_with_replacement_);
PUSH(stratification_method_);
PUSH(mtry_switch_);
PUSH(mtry_);
if(mtry_func_ != 0)
{
PUSH(1);
}
else
{
PUSH(0);
}
PUSH(tree_count_);
PUSH(min_split_node_size_);
PUSH(predict_weighted_);
#undef PUSH
}
void make_from_map(map_type & in) // -> const: .operator[] -> .find
{
#define PULL(item_, type_) item_ = type_(in[#item_][0]);
#define PULLBOOL(item_, type_) item_ = type_(in[#item_][0] > 0);
PULL(training_set_proportion_,double);
PULL(training_set_size_, int);
PULL(mtry_, int);
PULL(tree_count_, int);
PULL(min_split_node_size_, int);
PULLBOOL(sample_with_replacement_, bool);
PULLBOOL(prepare_online_learning_, bool);
PULLBOOL(predict_weighted_, bool);
PULL(training_set_calc_switch_, (RF_OptionTag)(int));
PULL(stratification_method_, (RF_OptionTag)(int));
PULL(mtry_switch_, (RF_OptionTag)(int));
/*don't pull*/
//PULL(mtry_func_!=0, int);
//PULL(training_set_func,int);
#undef PULL
#undef PULLBOOL
}
void make_map(map_type & in) const
{
#define PUSH(item_, type_) in[#item_] = double_array(1, double(item_));
#define PUSHFUNC(item_, type_) in[#item_] = double_array(1, double(item_!=0));
PUSH(training_set_proportion_,double);
PUSH(training_set_size_, int);
PUSH(mtry_, int);
PUSH(tree_count_, int);
PUSH(min_split_node_size_, int);
PUSH(sample_with_replacement_, bool);
PUSH(prepare_online_learning_, bool);
PUSH(predict_weighted_, bool);
PUSH(training_set_calc_switch_, RF_OptionTag);
PUSH(stratification_method_, RF_OptionTag);
PUSH(mtry_switch_, RF_OptionTag);
PUSHFUNC(mtry_func_, int);
PUSHFUNC(training_set_func_,int);
#undef PUSH
#undef PUSHFUNC
}
/**\brief create a RandomForestOptions object with default initialisation.
*
* look at the other member functions for more information on default
* values
*/
RandomForestOptions()
:
training_set_proportion_(1.0),
training_set_size_(0),
training_set_func_(0),
training_set_calc_switch_(RF_PROPORTIONAL),
sample_with_replacement_(true),
stratification_method_(RF_NONE),
mtry_switch_(RF_SQRT),
mtry_(0),
mtry_func_(0),
predict_weighted_(false),
tree_count_(256),
min_split_node_size_(1),
prepare_online_learning_(false)
{}
/**\brief specify stratification strategy
*
* default: RF_NONE
* possible values: RF_EQUAL, RF_PROPORTIONAL,
* RF_EXTERNAL, RF_NONE
* RF_EQUAL: get equal amount of samples per class.
* RF_PROPORTIONAL: sample proportional to fraction of class samples
* in population
* RF_EXTERNAL: strata_weights_ field of the ProblemSpec_t object
* has been set externally. (defunct)
*/
RandomForestOptions & use_stratification(RF_OptionTag in)
{
vigra_precondition(in == RF_EQUAL ||
in == RF_PROPORTIONAL ||
in == RF_EXTERNAL ||
in == RF_NONE,
"RandomForestOptions::use_stratification()"
"input must be RF_EQUAL, RF_PROPORTIONAL,"
"RF_EXTERNAL or RF_NONE");
stratification_method_ = in;
return *this;
}
RandomForestOptions & prepare_online_learning(bool in)
{
prepare_online_learning_=in;
return *this;
}
/**\brief sample from training population with or without replacement?
*
* <br> Default: true
*/
RandomForestOptions & sample_with_replacement(bool in)
{
sample_with_replacement_ = in;
return *this;
}
/**\brief specify the fraction of the total number of samples
* used per tree for learning.
*
* This value should be in [0.0 1.0] if sampling without
* replacement has been specified.
*
* <br> default : 1.0
*/
RandomForestOptions & samples_per_tree(double in)
{
training_set_proportion_ = in;
training_set_calc_switch_ = RF_PROPORTIONAL;
return *this;
}
/**\brief directly specify the number of samples per tree
*/
RandomForestOptions & samples_per_tree(int in)
{
training_set_size_ = in;
training_set_calc_switch_ = RF_CONST;
return *this;
}
/**\brief use external function to calculate the number of samples each
* tree should be learnt with.
*
* \param in function pointer that takes the number of rows in the
* learning data and outputs the number samples per tree.
*/
RandomForestOptions & samples_per_tree(int (*in)(int))
{
training_set_func_ = in;
training_set_calc_switch_ = RF_FUNCTION;
return *this;
}
/**\brief weight each tree with number of samples in that node
*/
RandomForestOptions & predict_weighted()
{
predict_weighted_ = true;
return *this;
}
/**\brief use built in mapping to calculate mtry
*
* Use one of the built in mappings to calculate mtry from the number
* of columns in the input feature data.
* \param in possible values: RF_LOG, RF_SQRT or RF_ALL
* <br> default: RF_SQRT.
*/
RandomForestOptions & features_per_node(RF_OptionTag in)
{
vigra_precondition(in == RF_LOG ||
in == RF_SQRT||
in == RF_ALL,
"RandomForestOptions()::features_per_node():"
"input must be of type RF_LOG or RF_SQRT");
mtry_switch_ = in;
return *this;
}
/**\brief Set mtry to a constant value
*
* mtry is the number of columns/variates/variables randomly chosen
* to select the best split from.
*
*/
RandomForestOptions & features_per_node(int in)
{
mtry_ = in;
mtry_switch_ = RF_CONST;
return *this;
}
/**\brief use a external function to calculate mtry
*
* \param in function pointer that takes int (number of columns
* of the and outputs int (mtry)
*/
RandomForestOptions & features_per_node(int(*in)(int))
{
mtry_func_ = in;
mtry_switch_ = RF_FUNCTION;
return *this;
}
/** How many trees to create?
*
* <br> Default: 255.
*/
RandomForestOptions & tree_count(int in)
{
tree_count_ = in;
return *this;
}
/**\brief Number of examples required for a node to be split.
*
* When the number of examples in a node is below this number,
* the node is not split even if class separation is not yet perfect.
* Instead, the node returns the proportion of each class
* (among the remaining examples) during the prediction phase.
* <br> Default: 1 (complete growing)
*/
RandomForestOptions & min_split_node_size(int in)
{
min_split_node_size_ = in;
return *this;
}
};
/** \brief problem types
*/
enum Problem_t{REGRESSION, CLASSIFICATION, CHECKLATER};
/** \brief problem specification class for the random forest.
*
* This class contains all the problem specific parameters the random
* forest needs for learning. Specification of an instance of this class
* is optional as all necessary fields will be computed prior to learning
* if not specified.
*
* if needed usage is similar to that of RandomForestOptions
*/
template<class LabelType = double>
class ProblemSpec
{
public:
/** \brief problem class
*/
typedef LabelType Label_t;
ArrayVector<Label_t> classes;
typedef ArrayVector<double> double_array;
typedef std::map<std::string, double_array> map_type;
int column_count_; // number of features
int class_count_; // number of classes
int row_count_; // number of samples
int actual_mtry_; // mtry used in training
int actual_msample_; // number if in-bag samples per tree
Problem_t problem_type_; // classification or regression
int used_; // this ProblemSpec is valid
ArrayVector<double> class_weights_; // if classes have different importance
int is_weighted_; // class_weights_ are used
double precision_; // termination criterion for regression loss
int response_size_;
template<class T>
void to_classlabel(int index, T & out) const
{
out = T(classes[index]);
}
template<class T>
int to_classIndex(T index) const
{
return std::find(classes.begin(), classes.end(), index) - classes.begin();
}
#define EQUALS(field) field(rhs.field)
ProblemSpec(ProblemSpec const & rhs)
:
EQUALS(column_count_),
EQUALS(class_count_),
EQUALS(row_count_),
EQUALS(actual_mtry_),
EQUALS(actual_msample_),
EQUALS(problem_type_),
EQUALS(used_),
EQUALS(class_weights_),
EQUALS(is_weighted_),
EQUALS(precision_),
EQUALS(response_size_)
{
std::back_insert_iterator<ArrayVector<Label_t> >
iter(classes);
std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
}
#undef EQUALS
#define EQUALS(field) field(rhs.field)
template<class T>
ProblemSpec(ProblemSpec<T> const & rhs)
:
EQUALS(column_count_),
EQUALS(class_count_),
EQUALS(row_count_),
EQUALS(actual_mtry_),
EQUALS(actual_msample_),
EQUALS(problem_type_),
EQUALS(used_),
EQUALS(class_weights_),
EQUALS(is_weighted_),
EQUALS(precision_),
EQUALS(response_size_)
{
std::back_insert_iterator<ArrayVector<Label_t> >
iter(classes);
std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
}
#undef EQUALS
#define EQUALS(field) (this->field = rhs.field);
ProblemSpec & operator=(ProblemSpec const & rhs)
{
EQUALS(column_count_);
EQUALS(class_count_);
EQUALS(row_count_);
EQUALS(actual_mtry_);
EQUALS(actual_msample_);
EQUALS(problem_type_);
EQUALS(used_);
EQUALS(is_weighted_);
EQUALS(precision_);
EQUALS(response_size_)
class_weights_.clear();
std::back_insert_iterator<ArrayVector<double> >
iter2(class_weights_);
std::copy(rhs.class_weights_.begin(), rhs.class_weights_.end(), iter2);
classes.clear();
std::back_insert_iterator<ArrayVector<Label_t> >
iter(classes);
std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
return *this;
}
template<class T>
ProblemSpec<Label_t> & operator=(ProblemSpec<T> const & rhs)
{
EQUALS(column_count_);
EQUALS(class_count_);
EQUALS(row_count_);
EQUALS(actual_mtry_);
EQUALS(actual_msample_);
EQUALS(problem_type_);
EQUALS(used_);
EQUALS(is_weighted_);
EQUALS(precision_);
EQUALS(response_size_)
class_weights_.clear();
std::back_insert_iterator<ArrayVector<double> >
iter2(class_weights_);
std::copy(rhs.class_weights_.begin(), rhs.class_weights_.end(), iter2);
classes.clear();
std::back_insert_iterator<ArrayVector<Label_t> >
iter(classes);
std::copy(rhs.classes.begin(), rhs.classes.end(), iter);
return *this;
}
#undef EQUALS
template<class T>
bool operator==(ProblemSpec<T> const & rhs)
{
bool result = true;
#define COMPARE(field) result = result && (this->field == rhs.field);
COMPARE(column_count_);
COMPARE(class_count_);
COMPARE(row_count_);
COMPARE(actual_mtry_);
COMPARE(actual_msample_);
COMPARE(problem_type_);
COMPARE(is_weighted_);
COMPARE(precision_);
COMPARE(used_);
COMPARE(class_weights_);
COMPARE(classes);
COMPARE(response_size_)
#undef COMPARE
return result;
}
bool operator!=(ProblemSpec & rhs)
{
return !(*this == rhs);
}
size_t serialized_size() const
{
return 10 + class_count_ *int(is_weighted_+1);
}
template<class Iter>
void unserialize(Iter const & begin, Iter const & end)
{
Iter iter = begin;
vigra_precondition(end - begin >= 10,
"ProblemSpec::unserialize():"
"wrong number of parameters");
#define PULL(item_, type_) item_ = type_(*iter); ++iter;
PULL(column_count_,int);
PULL(class_count_, int);
vigra_precondition(end - begin >= 10 + class_count_,
"ProblemSpec::unserialize(): 1");
PULL(row_count_, int);
PULL(actual_mtry_,int);
PULL(actual_msample_, int);
PULL(problem_type_, Problem_t);
PULL(is_weighted_, int);
PULL(used_, int);
PULL(precision_, double);
PULL(response_size_, int);
if(is_weighted_)
{
vigra_precondition(end - begin == 10 + 2*class_count_,
"ProblemSpec::unserialize(): 2");
class_weights_.insert(class_weights_.end(),
iter,
iter + class_count_);
iter += class_count_;
}
classes.insert(classes.end(), iter, end);
#undef PULL
}
template<class Iter>
void serialize(Iter const & begin, Iter const & end) const
{
Iter iter = begin;
vigra_precondition(end - begin == serialized_size(),
"RandomForestOptions::serialize():"
"wrong number of parameters");
#define PUSH(item_) *iter = double(item_); ++iter;
PUSH(column_count_);
PUSH(class_count_)
PUSH(row_count_);
PUSH(actual_mtry_);
PUSH(actual_msample_);
PUSH(problem_type_);
PUSH(is_weighted_);
PUSH(used_);
PUSH(precision_);
PUSH(response_size_);
if(is_weighted_)
{
std::copy(class_weights_.begin(),
class_weights_.end(),
iter);
iter += class_count_;
}
std::copy(classes.begin(),
classes.end(),
iter);
#undef PUSH
}
void make_from_map(map_type & in) // -> const: .operator[] -> .find
{
#define PULL(item_, type_) item_ = type_(in[#item_][0]);
PULL(column_count_,int);
PULL(class_count_, int);
PULL(row_count_, int);
PULL(actual_mtry_,int);
PULL(actual_msample_, int);
PULL(problem_type_, (Problem_t)int);
PULL(is_weighted_, int);
PULL(used_, int);
PULL(precision_, double);
PULL(response_size_, int);
class_weights_ = in["class_weights_"];
#undef PUSH
}
void make_map(map_type & in) const
{
#define PUSH(item_) in[#item_] = double_array(1, double(item_));
PUSH(column_count_);
PUSH(class_count_)
PUSH(row_count_);
PUSH(actual_mtry_);
PUSH(actual_msample_);
PUSH(problem_type_);
PUSH(is_weighted_);
PUSH(used_);
PUSH(precision_);
PUSH(response_size_);
in["class_weights_"] = class_weights_;
#undef PUSH
}
/**\brief set default values (-> values not set)
*/
ProblemSpec()
: column_count_(0),
class_count_(0),
row_count_(0),
actual_mtry_(0),
actual_msample_(0),
problem_type_(CHECKLATER),
used_(false),
is_weighted_(false),
precision_(0.0),
response_size_(1)
{}
ProblemSpec & column_count(int in)
{
column_count_ = in;
return *this;
}
/**\brief supply with class labels -
*
* the preprocessor will not calculate the labels needed in this case.
*/
template<class C_Iter>
ProblemSpec & classes_(C_Iter begin, C_Iter end)
{
int size = end-begin;
for(int k=0; k<size; ++k, ++begin)
classes.push_back(detail::RequiresExplicitCast<LabelType>::cast(*begin));
class_count_ = size;
return *this;
}
/** \brief supply with class weights -
*
* this is the only case where you would really have to
* create a ProblemSpec object.
*/
template<class W_Iter>
ProblemSpec & class_weights(W_Iter begin, W_Iter end)
{
class_weights_.insert(class_weights_.end(), begin, end);
is_weighted_ = true;
return *this;
}
void clear()
{
used_ = false;
classes.clear();
class_weights_.clear();
column_count_ = 0 ;
class_count_ = 0;
actual_mtry_ = 0;
actual_msample_ = 0;
problem_type_ = CHECKLATER;
is_weighted_ = false;
precision_ = 0.0;
response_size_ = 0;
}
bool used() const
{
return used_ != 0;
}
};
//@}
/**\brief Standard early stopping criterion
*
* Stop if region.size() < min_split_node_size_;
*/
class EarlyStoppStd
{
public:
int min_split_node_size_;
template<class Opt>
EarlyStoppStd(Opt opt)
: min_split_node_size_(opt.min_split_node_size_)
{}
template<class T>
void set_external_parameters(ProblemSpec<T>const &, int /* tree_count */ = 0, bool /* is_weighted_ */ = false)
{}
template<class Region>
bool operator()(Region& region)
{
return region.size() < min_split_node_size_;
}
template<class WeightIter, class T, class C>
bool after_prediction(WeightIter, int /* k */, MultiArrayView<2, T, C> /* prob */, double /* totalCt */)
{
return false;
}
};
} // namespace vigra
#endif //VIGRA_RF_COMMON_HXX
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