This class implements randomized CART algorithm used in the tree growing process of candidate trees in Random Forests algorithm. The tree growing process is different from the original CART algorithm because of the input attributes which are considered for each node split. In randomized CART, a few (fixed number) attributes are randomly chosen from all available attributes while deciding the best split. This is unlike the original CART where all available attributes are considered while deciding the best split. More...

#include <MyRandomCARTree.h>

Inheritance diagram for shogun::CMyRandomCARTree:

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Collaboration diagram for shogun::CMyRandomCARTree:

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Public Member Functions
	CMyRandomCARTree ()

virtual	~CMyRandomCARTree ()

virtual const char *	get_name () const

void	set_feature_subset_size (index_t size)

index_t	get_feature_subset_size () const

CMyRandomCARTree *	clone ()

Public Member Functions inherited from shogun::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). . 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 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)

Protected Member Functions
virtual index_t	compute_best_attribute (const SGMatrix< float64_t > &mat, const SGVector< float64_t > &weights, CDenseLabels *labels, SGVector< float64_t > &left, SGVector< float64_t > &right, SGVector< bool > &is_left_final, index_t &num_missing, index_t &count_left, index_t &count_right, float64_t &IG, index_t subset_size=0, const SGVector< index_t > &active_indices=SGVector< index_t >())

Protected Member Functions inherited from shogun::CMyCARTree
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)

Private Member Functions
void	init ()

Private Attributes
index_t	m_randsubset_size

Additional Inherited Members
Static Public Attributes inherited from shogun::CMyCARTree
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 Attributes inherited from shogun::CMyCARTree
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

This class implements randomized CART algorithm used in the tree growing process of candidate trees in Random Forests algorithm. The tree growing process is different from the original CART algorithm because of the input attributes which are considered for each node split. In randomized CART, a few (fixed number) attributes are randomly chosen from all available attributes while deciding the best split. This is unlike the original CART where all available attributes are considered while deciding the best split.

Definition at line 48 of file MyRandomCARTree.h.

Constructor & Destructor Documentation

◆ CMyRandomCARTree()

CMyRandomCARTree::CMyRandomCARTree ( )

constructor

Definition at line 36 of file MyRandomCARTree.cc.

◆ ~CMyRandomCARTree()

CMyRandomCARTree::~CMyRandomCARTree ( )

virtual

destructor

Definition at line 42 of file MyRandomCARTree.cc.

Member Function Documentation

◆ clone()

CMyRandomCARTree * CMyRandomCARTree::clone ( )

Definition at line 71 of file MyRandomCARTree.cc.

◆ compute_best_attribute()

index_t CMyRandomCARTree::compute_best_attribute	(	const SGMatrix< float64_t > &	mat,
		const SGVector< float64_t > &	weights,
		CDenseLabels *	labels,
		SGVector< float64_t > &	left,
		SGVector< float64_t > &	right,
		SGVector< bool > &	is_left_final,
		index_t &	num_missing,
		index_t &	count_left,
		index_t &	count_right,
		float64_t &	IG,
		index_t	subset_size = `0`,
		const SGVector< index_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

Returns: index to the best attribute

Definition at line 52 of file MyRandomCARTree.cc.

◆ get_feature_subset_size()

index_t shogun::CMyRandomCARTree::get_feature_subset_size ( ) const

inline

get number of random features to choose in each node split

Returns: size subset size

Definition at line 72 of file MyRandomCARTree.h.

◆ get_name()

virtual const char* shogun::CMyRandomCARTree::get_name ( ) const

inlinevirtual

get name

Returns: class name CARTree

Reimplemented from shogun::CMyCARTree.

Definition at line 60 of file MyRandomCARTree.h.

◆ init()

void CMyRandomCARTree::init ( )

private

initialize parameters

Definition at line 87 of file MyRandomCARTree.cc.

◆ set_feature_subset_size()

void CMyRandomCARTree::set_feature_subset_size ( index_t size )

set number of random features to choose in each node split

Parameters

size	subset size

Definition at line 46 of file MyRandomCARTree.cc.

Member Data Documentation

◆ m_randsubset_size

index_t shogun::CMyRandomCARTree::m_randsubset_size

private

random feature subset size

Definition at line 100 of file MyRandomCARTree.h.

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

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members