shogun::CMyCARTree Class Reference

#include <MyCARTree.h>

Inheritance diagram for shogun::CMyCARTree:

Collaboration diagram for shogun::CMyCARTree:

Public Member Functions
	CMyCARTree ()
	This class implements the Classification And Regression Trees algorithm by Breiman et al for decision tree learning. A CART tree is a binary decision tree that is constructed by splitting a node into two child nodes repeatedly, beginning with the root node that contains the whole dataset. TREE GROWING PROCESS : During the tree growing process, we recursively split a node into left child and right child so that the resulting nodes are "purest". We do this until any of the stopping criteria is met. To find the best split, we scan through all possible splits in all predictive attributes. The best split is one that maximises some splitting criterion. For classification tasks, ie. when the dependent attribute is categorical, the Gini index is used. For regression tasks, ie. when the dependent variable is continuous, least squares deviation is used. The algorithm uses two stopping criteria : if node becomes completely "pure", ie. all its members have identical dependent variable, or all of them have identical predictive attributes (independent variables). . More...
	CMyCARTree (SGVector< bool > attribute_types, EProblemType prob_type=PT_MULTICLASS)
	CMyCARTree (SGVector< bool > attribute_types, EProblemType prob_type, int32_t num_folds, bool cv_prune)
virtual	~CMyCARTree ()
virtual void	set_labels (CLabels *lab)
virtual const char *	get_name () const
virtual EProblemType	get_machine_problem_type () const
void	set_machine_problem_type (EProblemType mode)
virtual bool	is_label_valid (CLabels *lab) const
virtual CBinaryLabels *	apply_binary (CFeatures *data=NULL)
virtual CMulticlassLabels *	apply_multiclass (CFeatures *data=NULL)
virtual CRegressionLabels *	apply_regression (CFeatures *data=NULL)
void	prune_using_test_dataset (CDenseFeatures< float64_t > *feats, CLabels *gnd_truth, SGVector< float64_t > weights=SGVector< float64_t >())
void	set_weights (SGVector< float64_t > w)
SGVector< float64_t >	get_weights () const
void	clear_weights ()
void	set_feature_types (SGVector< bool > ft)
SGVector< bool >	get_feature_types () const
void	clear_feature_types ()
int32_t	get_num_folds () const
void	set_num_folds (int32_t folds)
int32_t	get_max_depth () const
void	set_max_depth (int32_t depth)
int32_t	get_min_node_size () const
void	set_min_node_size (int32_t nsize)
void	set_cv_pruning (bool cv_pruning)
float64_t	get_label_epsilon ()
void	set_label_epsilon (float64_t epsilon)
void	pre_sort_features (CFeatures *data, SGMatrix< float64_t > &sorted_feats, SGMatrix< index_t > &sorted_indices)
void	set_sorted_features (SGMatrix< float64_t > &sorted_feats, SGMatrix< index_t > &sorted_indices)
std::vector< double >	feature_importances ()
SGVector< float64_t >	get_certainty_vector () const
void	set_probabilities (CLabels *labels, CFeatures *data=NULL)

Static Public Attributes
static const float64_t	MISSING = CMath::MAX_REAL_NUMBER
static const float64_t	MIN_SPLIT_GAIN = 1e-7
static const float64_t	EQ_DELTA = 1e-7

Protected Member Functions
virtual bool	train_machine (CFeatures *data=NULL)
virtual CBinaryTreeMachineNode< MyCARTreeNodeData > *	CARTtrain (CFeatures *data, SGVector< float64_t > weights, CLabels *labels, int32_t level)
SGVector< float64_t >	get_unique_labels (SGVector< float64_t > labels_vec, int32_t &n_ulabels)
virtual int32_t	compute_best_attribute (const SGMatrix< float64_t > &mat, const SGVector< float64_t > &weights, CLabels *labels, SGVector< float64_t > &left, SGVector< float64_t > &right, SGVector< bool > &is_left_final, int32_t &num_missing, int32_t &count_left, int32_t &count_right, float64_t &IG, int32_t subset_size=0, const SGVector< int32_t > &active_indices=SGVector< index_t >())
SGVector< bool >	surrogate_split (SGMatrix< float64_t > data, SGVector< float64_t > weights, SGVector< bool > nm_left, int32_t attr)
void	handle_missing_vecs_for_continuous_surrogate (SGMatrix< float64_t > m, CDynamicArray< int32_t > *missing_vecs, CDynamicArray< float64_t > *association_index, CDynamicArray< int32_t > *intersect_vecs, SGVector< bool > is_left, SGVector< float64_t > weights, float64_t p, int32_t attr)
void	handle_missing_vecs_for_nominal_surrogate (SGMatrix< float64_t > m, CDynamicArray< int32_t > *missing_vecs, CDynamicArray< float64_t > *association_index, CDynamicArray< int32_t > *intersect_vecs, SGVector< bool > is_left, SGVector< float64_t > weights, float64_t p, int32_t attr)
float64_t	gain (SGVector< float64_t > wleft, SGVector< float64_t > wright, SGVector< float64_t > wtotal, SGVector< float64_t > labels)
float64_t	gain (const SGVector< float64_t > &wleft, const SGVector< float64_t > &wright, const SGVector< float64_t > &wtotal)
float64_t	gini_impurity_index (const SGVector< float64_t > &weighted_lab_classes, float64_t &total_weight)
float64_t	least_squares_deviation (const SGVector< float64_t > &labels, const SGVector< float64_t > &weights, float64_t &total_weight)
CLabels *	apply_from_current_node (CDenseFeatures< float64_t > *feats, bnode_t *current, bool set_certainty=false)
void	prune_by_cross_validation (CDenseFeatures< float64_t > *data, int32_t folds)
float64_t	compute_error (CLabels *labels, CLabels *reference, SGVector< float64_t > weights)
CDynamicObjectArray *	prune_tree (CTreeMachine< MyCARTreeNodeData > *tree)
float64_t	find_weakest_alpha (bnode_t *node)
void	cut_weakest_link (bnode_t *node, float64_t alpha)
void	form_t1 (bnode_t *node)
void	init ()
void	get_importance (bnode_t *node, vector< double > &importances)

Protected Attributes
float64_t	m_label_epsilon
SGVector< bool >	m_nominal
SGVector< float64_t >	m_weights
SGMatrix< float64_t >	m_sorted_features
SGMatrix< index_t >	m_sorted_indices
bool	m_pre_sort
bool	m_types_set
bool	m_weights_set
bool	m_apply_cv_pruning
int32_t	m_folds
EProblemType	m_mode
CDynamicArray< float64_t > *	m_alphas
int32_t	m_max_depth
int32_t	m_min_node_size
SGVector< float64_t >	m_certainty

Detailed Description

Definition at line 47 of file MyCARTree.h.

Constructor & Destructor Documentation

CMyCARTree() [1/3]

CMyCARTree::CMyCARTree

(

)

This class implements the Classification And Regression Trees algorithm by Breiman et al for decision tree learning. A CART tree is a binary decision tree that is constructed by splitting a node into two child nodes repeatedly, beginning with the root node that contains the whole dataset.

TREE GROWING PROCESS :
During the tree growing process, we recursively split a node into left child and right child so that the resulting nodes are "purest". We do this until any of the stopping criteria is met. To find the best split, we scan through all possible splits in all predictive attributes. The best split is one that maximises some splitting criterion. For classification tasks, ie. when the dependent attribute is categorical, the Gini index is used. For regression tasks, ie. when the dependent variable is continuous, least squares deviation is used. The algorithm uses two stopping criteria : if node becomes completely "pure", ie. all its members have identical dependent variable, or all of them have identical predictive attributes (independent variables).

.

COST-COMPLEXITY PRUNING :
The maximal tree, \(T_max\) grown during tree growing process is bound to overfit. Hence pruning becomes necessary. Cost-Complexity pruning yields a list of subtrees of varying depths using the complexity normalized resubstitution error, \(R_\alpha(T)\). The resubstitution error R(T) is a measure of how well a decision tree fits the training data. This measure favours larger trees over smaller ones. However, complexity normalized resubstitution error, adds penalty for increased complexity and hence counters overfitting.
\(R_\alpha(T)=R(T)+\alpha \times (numleaves)\)
The best subtree among the list of subtrees can be chosen using cross validation or using best-fit in the test dataset.
cf. https://onlinecourses.science.psu.edu/stat557/node/93

HANDLING MISSING VALUES :
While choosing the best split at a node, missing attribute values are left out. But data vectors with missing values of the best attribute chosen are sent to left child or right child using a surrogate split. A surrogate split is one that imitates the best split as closely as possible. While choosing a surrogate split, all splits alternative to the best split are scaned and the degree of closeness between the two is measured using a metric called predictive measure of association, \(\lambda_{i,j}\).
\(\lambda_{i,j} = \frac{min(P_L,P_R)-(1-P_{L_iL_j}-P_{R_iR_j})}{min(P_L,P_R)}\)
where \(P_L\) and \(P_R\) are the node probabilities for the optimal split of node i into left and right nodes respectively, \(P_{L_iL_j}\) ( \(P_{R_iR_j}\) resp.) is the probability that both (optimal) node i and (surrogate) node j send an observation to the Left (Right resp.).
We use best surrogate split, 2nd best surrogate split and so on until all data points with missing attributes in a node have been sent to left/right child. If all possible surrogate splits are used up but some data points are still to be assigned left/right child, majority rule is used, ie. the data points are assigned the child where majority of data points have gone from the node.
cf. http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_tree-cart.htm default constructor

Definition at line 56 of file MyCARTree.cc.

CMyCARTree() [2/3]

CMyCARTree::CMyCARTree	(	SGVector< bool >	attribute_types,
		EProblemType	prob_type = PT_MULTICLASS
	)

constructor

Parameters

attribute_types	type of each predictive attribute (true for nominal, false for ordinal/continuous)
prob_type	machine problem type - PT_MULTICLASS or PT_REGRESSION

Definition at line 62 of file MyCARTree.cc.

CMyCARTree() [3/3]

CMyCARTree::CMyCARTree	(	SGVector< bool >	attribute_types,
		EProblemType	prob_type,
		int32_t	num_folds,
		bool	cv_prune
	)

constructor - to be used while using cross-validation pruning

Parameters

attribute_types	type of each predictive attribute (true for nominal, false for ordinal/continuous)
prob_type	machine problem type - PT_MULTICLASS or PT_REGRESSION
num_folds	number of subsets used in cross-valiation
cv_prune	- whether to use cross-validation pruning

Definition at line 70 of file MyCARTree.cc.

~CMyCARTree()

CMyCARTree::~CMyCARTree ( )

virtual

destructor

Definition at line 81 of file MyCARTree.cc.

Member Function Documentation

apply_binary()

CBinaryLabels * CMyCARTree::apply_binary ( CFeatures *  data = NULL )

virtual

WGL: classify data using Classification Tree

Parameters

data	data to be classified

Returns

BinaryLabels corresponding to labels of various test vectors

Definition at line 115 of file MyCARTree.cc.

apply_from_current_node()

CLabels * CMyCARTree::apply_from_current_node ( CDenseFeatures< float64_t > *  feats, bnode_t *  current, bool  set_certainty = false  )

protected

uses current subtree to classify/regress data

Parameters

feats	data to be classified/regressed
current	root of current subtree

Returns

classification/regression labels of input data

Definition at line 1144 of file MyCARTree.cc.

apply_multiclass()

CMulticlassLabels * CMyCARTree::apply_multiclass ( CFeatures *  data = NULL )

virtual

classify data using Classification Tree

Parameters

data	data to be classified

Returns

MulticlassLabels corresponding to labels of various test vectors

Definition at line 136 of file MyCARTree.cc.

apply_regression()

CRegressionLabels * CMyCARTree::apply_regression ( CFeatures *  data = NULL )

virtual

Get regression labels using Regression Tree

Parameters

data	data whose regression output is needed

Returns

Regression output for various test vectors

Definition at line 150 of file MyCARTree.cc.

CARTtrain()

CBinaryTreeMachineNode< MyCARTreeNodeData > * CMyCARTree::CARTtrain ( CFeatures *  data, SGVector< float64_t >  weights, CLabels *  labels, int32_t  level  )

protectedvirtual

CARTtrain - recursive CART training method

Parameters

data	training data
weights	vector of weights of data points
labels	labels of data points
level	current tree depth

Returns

pointer to the root of the CART subtree

Definition at line 351 of file MyCARTree.cc.

clear_feature_types()

void CMyCARTree::clear_feature_types

(

)

clear feature types of various features

Definition at line 236 of file MyCARTree.cc.

clear_weights()

void CMyCARTree::clear_weights

(

)

clear weights of data points

Definition at line 219 of file MyCARTree.cc.

compute_best_attribute()

int32_t CMyCARTree::compute_best_attribute ( const SGMatrix< float64_t > &  mat, const SGVector< float64_t > &  weights, CLabels *  labels, SGVector< float64_t > &  left, SGVector< float64_t > &  right, SGVector< bool > &  is_left_final, int32_t &  num_missing, int32_t &  count_left, int32_t &  count_right, float64_t &  IG, int32_t  subset_size = 0, const SGVector< int32_t > &  active_indices = SGVector<index_t>()  )

protectedvirtual

computes best attribute for CARTtrain

Parameters

mat	data matrix
weights	data weights
labels_vec	data labels
left	stores feature values for left transition
right	stores feature values for right transition
is_left_final	stores which feature vectors go to the left child
num_missing	number of missing attributes
count_left	stores number of feature values for left transition
count_right	stores number of feature values for right transition
IG	(WGL): store impurity gain at node

Returns

index to the best attribute

Definition at line 570 of file MyCARTree.cc.

compute_error()

float64_t CMyCARTree::compute_error ( CLabels *  labels, CLabels *  reference, SGVector< float64_t >  weights  )

protected

computes error in classification/regression for classification it eveluates weight_missclassified/total_weight for regression it evaluates weighted sum of squared error/total_weight

Parameters

labels	the labels whose error needs to be calculated
reference	actual labels against which test labels are compared
weights	weights associated with the labels

Returns

error evaluated

Definition at line 1382 of file MyCARTree.cc.

cut_weakest_link()

void CMyCARTree::cut_weakest_link ( bnode_t *  node, float64_t  alpha  )

protected

recursively cuts weakest link(s) in a tree

Parameters

node	the root of subtree whose weakest link it cuts
alpha	alpha value corresponding to weakest link

Definition at line 1491 of file MyCARTree.cc.

feature_importances()

vector< double > CMyCARTree::feature_importances

(

)

WGL: return Gini importance scores for features

Importance is defined as the sum across all splits in the tree of the information criterion brought about by each feature.

Definition at line 1580 of file MyCARTree.cc.

find_weakest_alpha()

float64_t CMyCARTree::find_weakest_alpha ( bnode_t *  node )

protected

recursively finds alpha corresponding to weakest link(s)

Parameters

node	the root of subtree whose weakest link it finds

Returns

alpha value corresponding to the weakest link in subtree

Definition at line 1470 of file MyCARTree.cc.

form_t1()

void CMyCARTree::form_t1 ( bnode_t *  node )

protected

recursively forms base case $ft_1$f tree from $ft_max$f during pruning

Parameters

node	the root of current subtree

Definition at line 1521 of file MyCARTree.cc.

gain() [1/2]

float64_t CMyCARTree::gain ( const SGVector< float64_t > &  wleft, const SGVector< float64_t > &  wright, const SGVector< float64_t > &  wtotal  )

protected

returns gain in Gini impurity measure

Parameters

wleft	left child label distribution
wright	right child label distribution
wtotal	label distribution in current node

Returns

Gini gain achieved after spliting the node

Definition at line 1107 of file MyCARTree.cc.

gain() [2/2]

float64_t CMyCARTree::gain ( SGVector< float64_t >  wleft, SGVector< float64_t >  wright, SGVector< float64_t >  wtotal, SGVector< float64_t >  labels  )

protected

returns gain in regression case

Parameters

wleft	left child weight distribution
wright	right child weights distribution
wtotal	weight distribution in current node
labels	regression labels

Returns

least squared deviation gain achieved after spliting the node

Definition at line 1093 of file MyCARTree.cc.

get_certainty_vector()

SGVector< float64_t > CMyCARTree::get_certainty_vector

(

)

const

WGL: gets the probability estimate for each sample TODO: make this set_probabilities

Definition at line 1238 of file MyCARTree.cc.

get_feature_types()

SGVector< bool > CMyCARTree::get_feature_types

(

)

const

set feature types of various features

Returns

bool vector - true for nominal feature false for continuous feature type

Definition at line 231 of file MyCARTree.cc.

get_importance()

void CMyCARTree::get_importance ( bnode_t *  node, vector< double > &  importances  )

protected

WGL: recursive function for getting node importance

Definition at line 1597 of file MyCARTree.cc.

get_label_epsilon()

float64_t CMyCARTree::get_label_epsilon

(

)

get label epsilon

Returns

equality range for regression labels

Definition at line 50 of file MyCARTree.cc.

get_machine_problem_type()

EProblemType CMyCARTree::get_machine_problem_type ( ) const

virtual

get problem type - multiclass classification or regression

Returns

PT_MULTICLASS or PT_REGRESSION

Definition at line 42 of file MyCARTree.cc.

get_max_depth()

int32_t CMyCARTree::get_max_depth

(

)

const

get max allowed tree depth

Returns

max allowed tree depth

Definition at line 253 of file MyCARTree.cc.

get_min_node_size()

int32_t CMyCARTree::get_min_node_size

(

)

const

get min allowed node size

Returns

min allowed node size

Definition at line 264 of file MyCARTree.cc.

get_name()

const char * CMyCARTree::get_name ( ) const

virtual

get name

Returns

class name CARTree

Reimplemented in shogun::CMyRandomCARTree.

Definition at line 40 of file MyCARTree.cc.

get_num_folds()

int32_t CMyCARTree::get_num_folds

(

)

const

get number of subsets used for cross validation

Returns

number of folds used in cross validation

Definition at line 242 of file MyCARTree.cc.

get_unique_labels()

SGVector< float64_t > CMyCARTree::get_unique_labels ( SGVector< float64_t >  labels_vec, int32_t &  n_ulabels  )

protected

modify labels for compute_best_attribute

Parameters

labels_vec	labels vector
n_ulabels	stores number of unique labels

Returns

unique labels

Definition at line 546 of file MyCARTree.cc.

get_weights()

SGVector< float64_t > CMyCARTree::get_weights

(

)

const

get weights of data points

Returns

vector of weights

Definition at line 214 of file MyCARTree.cc.

gini_impurity_index()

float64_t CMyCARTree::gini_impurity_index ( const SGVector< float64_t > &  weighted_lab_classes, float64_t &  total_weight  )

protected

returns Gini impurity of a node

Parameters

weighted_lab_classes	vector of weights associated with various labels
total_weight	stores the total weight of all classes

Returns

Gini index of the node

Definition at line 1119 of file MyCARTree.cc.

handle_missing_vecs_for_continuous_surrogate()

void CMyCARTree::handle_missing_vecs_for_continuous_surrogate ( SGMatrix< float64_t >  m, CDynamicArray< int32_t > *  missing_vecs, CDynamicArray< float64_t > *  association_index, CDynamicArray< int32_t > *  intersect_vecs, SGVector< bool >  is_left, SGVector< float64_t >  weights, float64_t  p, int32_t  attr  )

protected

handles missing values for a chosen continuous surrogate attribute

Parameters

m	training data matrix
missing_vecs	column indices of vectors with missing attribute in data matrix
association_index	stores the final lambda values used to address members of missing_vecs
intersect_vecs	column indices of vectors with known values for the best attribute as well as the chosen surrogate
is_left	whether a vector goes into left child
weights	weights of training data vectors
p	min(p_l,p_r) in the lambda formula
attr	surrogate attribute chosen for split

Returns

vector denoting whether a data point goes to left child for all data points including ones with missing attributes

Definition at line 956 of file MyCARTree.cc.

handle_missing_vecs_for_nominal_surrogate()

void CMyCARTree::handle_missing_vecs_for_nominal_surrogate ( SGMatrix< float64_t >  m, CDynamicArray< int32_t > *  missing_vecs, CDynamicArray< float64_t > *  association_index, CDynamicArray< int32_t > *  intersect_vecs, SGVector< bool >  is_left, SGVector< float64_t >  weights, float64_t  p, int32_t  attr  )

protected

handles missing values for a chosen nominal surrogate attribute

Parameters

m	training data matrix
missing_vecs	column indices of vectors with missing attribute in data matrix
association_index	stores the final lambda values used to address members of missing_vecs
intersect_vecs	column indices of vectors with known values for the best attribute as well as the chosen surrogate
is_left	whether a vector goes into left child
weights	weights of training data vectors
p	min(p_l,p_r) in the lambda formula
attr	surrogate attribute chosen for split

Returns

vector denoting whether a data point goes to left child for all data points including ones with missing attributes

Definition at line 1013 of file MyCARTree.cc.

init()

void CMyCARTree::init ( )

protected

initializes members of class

Definition at line 1547 of file MyCARTree.cc.

is_label_valid()

bool CMyCARTree::is_label_valid ( CLabels *  lab ) const

virtual

whether labels supplied are valid for current problem type

Parameters

lab	labels supplied

Returns

true for valid labels, false for invalid labels

Definition at line 105 of file MyCARTree.cc.

least_squares_deviation()

float64_t CMyCARTree::least_squares_deviation ( const SGVector< float64_t > &  labels, const SGVector< float64_t > &  weights, float64_t &  total_weight  )

protected

returns least squares deviation

Parameters

labels	regression labels
weights	weights of regression data points
total_weight	stores sum of weights in weights vector

Returns

least squares deviation of the data

Definition at line 1129 of file MyCARTree.cc.

pre_sort_features()

void CMyCARTree::pre_sort_features	(	CFeatures *	data,
		SGMatrix< float64_t > &	sorted_feats,
		SGMatrix< index_t > &	sorted_indices
	)

Definition at line 331 of file MyCARTree.cc.

prune_by_cross_validation()

void CMyCARTree::prune_by_cross_validation ( CDenseFeatures< float64_t > *  data, int32_t  folds  )

protected

prune by cross validation

Parameters

data	training data
folds	the integer V for V-fold cross validation

Definition at line 1243 of file MyCARTree.cc.

prune_tree()

CDynamicObjectArray * CMyCARTree::prune_tree ( CTreeMachine< MyCARTreeNodeData > *  tree )

protected

cost-complexity pruning

Parameters

tree	the tree to be pruned

Returns

CDynamicObjectArray of pruned trees

Definition at line 1420 of file MyCARTree.cc.

prune_using_test_dataset()

void CMyCARTree::prune_using_test_dataset	(	CDenseFeatures< float64_t > *	feats,
		CLabels *	gnd_truth,
		SGVector< float64_t >	weights = SGVector<float64_t>()
	)

uses test dataset to choose best pruned subtree

Parameters

feats	test data to be used
gnd_truth	test labels
weights	weights of data points

Definition at line 162 of file MyCARTree.cc.

set_cv_pruning()

void CMyCARTree::set_cv_pruning

(

bool

cv_pruning

)

Set cross validation pruning parameter

Parameters

cv_pruning

allow CV pruning

Definition at line 45 of file MyCARTree.cc.

set_feature_types()

void CMyCARTree::set_feature_types

(

SGVector< bool >

ft

)

set feature types of various features

Parameters

ft	bool vector true for nominal feature false for continuous feature type

Definition at line 225 of file MyCARTree.cc.

set_label_epsilon()

void CMyCARTree::set_label_epsilon

(

float64_t

epsilon

)

set label epsilon

Parameters

epsilon

equality range for regression labels

Definition at line 275 of file MyCARTree.cc.

set_labels()

void CMyCARTree::set_labels ( CLabels *  lab )

virtual

set labels - automagically switch machine problem type based on type of labels supplied

Parameters

lab

labels

Definition at line 86 of file MyCARTree.cc.

set_machine_problem_type()

void CMyCARTree::set_machine_problem_type

(

EProblemType

mode

)

set problem type - multiclass classification or regression

Parameters

mode	EProblemType PT_MULTICLASS or PT_REGRESSION

Definition at line 100 of file MyCARTree.cc.

set_max_depth()

void CMyCARTree::set_max_depth

(

int32_t

depth

)

set max allowed tree depth

Parameters

depth

max allowed tree depth

Definition at line 258 of file MyCARTree.cc.

set_min_node_size()

void CMyCARTree::set_min_node_size

(

int32_t

nsize

)

set min allowed node size

Parameters

nsize

min allowed node size

Definition at line 269 of file MyCARTree.cc.

set_num_folds()

void CMyCARTree::set_num_folds

(

int32_t

folds

)

set number of subsets for cross validation

Parameters

folds

number of folds used in cross validation

Definition at line 247 of file MyCARTree.cc.

set_probabilities()

void CMyCARTree::set_probabilities	(	CLabels *	labels,
		CFeatures *	data = NULL
	)

WGL: sets the probabilities on each label according to m_certainty

Definition at line 1615 of file MyCARTree.cc.

set_sorted_features()

void CMyCARTree::set_sorted_features	(	SGMatrix< float64_t > &	sorted_feats,
		SGMatrix< index_t > &	sorted_indices
	)

Definition at line 324 of file MyCARTree.cc.

set_weights()

void CMyCARTree::set_weights

(

SGVector< float64_t >

w

)

set weights of data points

Parameters

w	vector of weights

Definition at line 208 of file MyCARTree.cc.

surrogate_split()

SGVector< bool > CMyCARTree::surrogate_split ( SGMatrix< float64_t >  data, SGVector< float64_t >  weights, SGVector< bool >  nm_left, int32_t  attr  )

protected

handles missing values through surrogate splits

Parameters

data	training data matrix
weights	vector of weights of data points
nm_left	whether a data point is put into left child (available for only data points with non-missing attribute attr)
attr	best attribute chosen for split

Returns

vector denoting whether a data point goes to left child for all data points including ones with missing attributes

Definition at line 881 of file MyCARTree.cc.

train_machine()

bool CMyCARTree::train_machine ( CFeatures *  data = NULL )

protectedvirtual

train machine - build CART from training data

Parameters

data	training data

Returns

true

Definition at line 281 of file MyCARTree.cc.

Member Data Documentation

EQ_DELTA

const float64_t CMyCARTree::EQ_DELTA = 1e-7

static

equality epsilon

Definition at line 447 of file MyCARTree.h.

m_alphas

CDynamicArray<float64_t>* shogun::CMyCARTree::m_alphas

protected

stores \(\alpha_k\) values evaluated in cost-complexity pruning

Definition at line 484 of file MyCARTree.h.

m_apply_cv_pruning

bool shogun::CMyCARTree::m_apply_cv_pruning

protected

flag indicating whether cross validation pruning has to be applied or not - false by default

Definition at line 475 of file MyCARTree.h.

m_certainty

SGVector<float64_t> shogun::CMyCARTree::m_certainty

protected

percentage of certainty of labels predicted by decision tree ie. weight of elements belonging to predicted class in a node/ total weight in a node

Definition at line 495 of file MyCARTree.h.

m_folds

int32_t shogun::CMyCARTree::m_folds

protected

V in V-fold cross validation - 5 by default

Definition at line 478 of file MyCARTree.h.

m_label_epsilon

float64_t shogun::CMyCARTree::m_label_epsilon

protected

equality range for regression labels

Definition at line 451 of file MyCARTree.h.

m_max_depth

int32_t shogun::CMyCARTree::m_max_depth

protected

max allowed depth of tree

Definition at line 487 of file MyCARTree.h.

m_min_node_size

int32_t shogun::CMyCARTree::m_min_node_size

protected

minimum number of feature vectors required in a node

Definition at line 490 of file MyCARTree.h.

m_mode

EProblemType shogun::CMyCARTree::m_mode

protected

Problem type : PT_MULTICLASS or PT_REGRESSION

Definition at line 481 of file MyCARTree.h.

m_nominal

SGVector<bool> shogun::CMyCARTree::m_nominal

protected

vector depicting whether various feature dimensions are nominal or not

Definition at line 454 of file MyCARTree.h.

m_pre_sort

bool shogun::CMyCARTree::m_pre_sort

protected

If pre sorted features are used in train

Definition at line 466 of file MyCARTree.h.

m_sorted_features

SGMatrix<float64_t> shogun::CMyCARTree::m_sorted_features

protected

sorted transposed features

Definition at line 460 of file MyCARTree.h.

m_sorted_indices

SGMatrix<index_t> shogun::CMyCARTree::m_sorted_indices

protected

sorted indices

Definition at line 463 of file MyCARTree.h.

m_types_set

bool shogun::CMyCARTree::m_types_set

protected

flag storing whether the type of various feature dimensions are specified using is_nominal_feature

Definition at line 469 of file MyCARTree.h.

m_weights

SGVector<float64_t> shogun::CMyCARTree::m_weights

protected

weights of samples in training set

Definition at line 457 of file MyCARTree.h.

m_weights_set

bool shogun::CMyCARTree::m_weights_set

protected

flag storing whether weights of samples are specified using weights vector

Definition at line 472 of file MyCARTree.h.

MIN_SPLIT_GAIN

const float64_t CMyCARTree::MIN_SPLIT_GAIN = 1e-7

static

min gain for splitting to be allowed

Definition at line 444 of file MyCARTree.h.

MISSING

const float64_t CMyCARTree::MISSING = CMath::MAX_REAL_NUMBER

static

denotes that a feature in a vector is missing MISSING = NOT_A_NUMBER

Definition at line 441 of file MyCARTree.h.

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The documentation for this class was generated from the following files:

• MyCARTree.h
• MyCARTree.cc