SHOGUN  6.1.3
CCARTree Class Reference

Detailed Description

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

Definition at line 79 of file CARTree.h.

Inheritance diagram for CCARTree:
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Public Types

typedef CTreeMachineNode< CARTreeNodeDatanode_t

typedef CBinaryTreeMachineNode< CARTreeNodeDatabnode_t

typedef rxcpp::subjects::subject< ObservedValueSGSubject

typedef rxcpp::observable< ObservedValue, rxcpp::dynamic_observable< ObservedValue > > SGObservable

typedef rxcpp::subscriber< ObservedValue, rxcpp::observer< ObservedValue, void, void, void, void > > SGSubscriber

Public Member Functions

CCARTree ()

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

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

virtual ~CCARTree ()

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 CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)

virtual CRegressionLabelsapply_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_tget_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)

void set_root (CTreeMachineNode< CARTreeNodeData > *root)

CTreeMachineNode< CARTreeNodeData > * get_root ()

CTreeMachineclone_tree ()

int32_t get_num_machines () const

virtual bool train (CFeatures *data=NULL)

virtual CLabelsapply (CFeatures *data=NULL)

virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)

virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)

virtual CLatentLabelsapply_latent (CFeatures *data=NULL)

virtual CLabelsget_labels ()

void set_max_train_time (float64_t t)

float64_t get_max_train_time ()

virtual EMachineType get_classifier_type ()

void set_solver_type (ESolverType st)

ESolverType get_solver_type ()

virtual void set_store_model_features (bool store_model)

virtual bool train_locked (SGVector< index_t > indices)

virtual float64_t apply_one (int32_t i)

virtual CLabelsapply_locked (SGVector< index_t > indices)

virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)

virtual CRegressionLabelsapply_locked_regression (SGVector< index_t > indices)

virtual CMulticlassLabelsapply_locked_multiclass (SGVector< index_t > indices)

virtual CStructuredLabelsapply_locked_structured (SGVector< index_t > indices)

virtual CLatentLabelsapply_locked_latent (SGVector< index_t > indices)

virtual void data_lock (CLabels *labs, CFeatures *features)

virtual void post_lock (CLabels *labs, CFeatures *features)

virtual void data_unlock ()

virtual bool supports_locking () const

bool is_data_locked () const

SG_FORCED_INLINE bool cancel_computation () const

SG_FORCED_INLINE void pause_computation ()

SG_FORCED_INLINE void resume_computation ()

int32_t ref ()

int32_t ref_count ()

int32_t unref ()

virtual CSGObjectshallow_copy () const

virtual CSGObjectdeep_copy () const

virtual bool is_generic (EPrimitiveType *generic) const

template<class T >
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

void unset_generic ()

virtual void print_serializable (const char *prefix="")

virtual bool save_serializable (CSerializableFile *file, const char *prefix="")

virtual bool load_serializable (CSerializableFile *file, const char *prefix="")

void set_global_io (SGIO *io)

SGIOget_global_io ()

void set_global_parallel (Parallel *parallel)

Parallelget_global_parallel ()

void set_global_version (Version *version)

Versionget_global_version ()

SGStringList< char > get_modelsel_names ()

void print_modsel_params ()

char * get_modsel_param_descr (const char *param_name)

index_t get_modsel_param_index (const char *param_name)

void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject *> *dict)

bool has (const std::string &name) const

template<typename T >
bool has (const Tag< T > &tag) const

template<typename T , typename U = void>
bool has (const std::string &name) const

template<typename T >
void set (const Tag< T > &_tag, const T &value)

template<typename T , typename U = void>
void set (const std::string &name, const T &value)

template<typename T >
get (const Tag< T > &_tag) const

template<typename T , typename U = void>
get (const std::string &name) const

SGObservableget_parameters_observable ()

void subscribe_to_parameters (ParameterObserverInterface *obs)

void list_observable_parameters ()

virtual void update_parameter_hash ()

virtual bool parameter_hash_changed ()

virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)

virtual CSGObjectclone ()

Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

uint32_t m_hash

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< CARTreeNodeData > * CARTtrain (CFeatures *data, SGVector< float64_t > weights, CLabels *labels, int32_t level)

SGVector< float64_tget_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, 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)

CLabelsapply_from_current_node (CDenseFeatures< float64_t > *feats, bnode_t *current)

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

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

CDynamicObjectArrayprune_tree (CTreeMachine< CARTreeNodeData > *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 ()

virtual void store_model_features ()

virtual bool train_require_labels () const

rxcpp::subscription connect_to_signal_handler ()

void reset_computation_variables ()

virtual void on_next ()

virtual void on_pause ()

virtual void on_complete ()

virtual void load_serializable_pre () throw (ShogunException)

virtual void load_serializable_post () throw (ShogunException)

virtual void save_serializable_pre () throw (ShogunException)

virtual void save_serializable_post () throw (ShogunException)

template<typename T >
void register_param (Tag< T > &_tag, const T &value)

template<typename T >
void register_param (const std::string &name, const T &value)

bool clone_parameters (CSGObject *other)

void observe (const ObservedValue value)

void register_observable_param (const std::string &name, const SG_OBS_VALUE_TYPE type, const std::string &description)

Protected Attributes

float64_t m_label_epsilon

SGVector< bool > m_nominal

SGVector< float64_tm_weights

SGMatrix< float64_tm_sorted_features

SGMatrix< index_tm_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

CTreeMachineNode< CARTreeNodeData > * m_root

CDynamicObjectArraym_machines

float64_t m_max_train_time

CLabelsm_labels

ESolverType m_solver_type

bool m_store_model_features

bool m_data_locked

std::atomic< bool > m_cancel_computation

std::atomic< bool > m_pause_computation_flag

std::condition_variable m_pause_computation

std::mutex m_mutex

◆ bnode_t

 typedef CBinaryTreeMachineNode bnode_t
inherited

bnode_t type- Tree node with max 2 possible children

Definition at line 55 of file TreeMachine.h.

◆ node_t

 typedef CTreeMachineNode node_t
inherited

node_t type- Tree node with many possible children

Definition at line 52 of file TreeMachine.h.

◆ SGObservable

 inherited

Definition at line 130 of file SGObject.h.

◆ SGSubject

 inherited

Definition at line 127 of file SGObject.h.

◆ SGSubscriber

 typedef rxcpp::subscriber< ObservedValue, rxcpp::observer > SGSubscriber
inherited

Definition at line 133 of file SGObject.h.

◆ CCARTree() [1/3]

 CCARTree ( )

default constructor

Definition at line 42 of file CARTree.cpp.

◆ CCARTree() [2/3]

 CCARTree ( 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 48 of file CARTree.cpp.

◆ CCARTree() [3/3]

 CCARTree ( 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 56 of file CARTree.cpp.

◆ ~CCARTree()

 ~CCARTree ( )
virtual

destructor

Definition at line 67 of file CARTree.cpp.

◆ apply()

 CLabels * apply ( CFeatures * data = NULL )
virtualinherited

apply machine to data if data is not specified apply to the current features

Parameters
 data (test)data to be classified
Returns
classified labels

Definition at line 159 of file Machine.cpp.

◆ apply_binary()

 CBinaryLabels * apply_binary ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of binary classification problem

Definition at line 215 of file Machine.cpp.

◆ apply_from_current_node()

 CLabels * apply_from_current_node ( CDenseFeatures< float64_t > * feats, bnode_t * current )
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 1104 of file CARTree.cpp.

◆ apply_latent()

 CLatentLabels * apply_latent ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 239 of file Machine.cpp.

◆ apply_locked()

 CLabels * apply_locked ( SGVector< index_t > indices )
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked

Parameters
 indices index vector (of locked features) that is predicted

Definition at line 194 of file Machine.cpp.

◆ apply_locked_binary()

 CBinaryLabels * apply_locked_binary ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for binary problems

Reimplemented in CKernelMachine.

Definition at line 245 of file Machine.cpp.

◆ apply_locked_latent()

 CLatentLabels * apply_locked_latent ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for latent problems

Definition at line 273 of file Machine.cpp.

◆ apply_locked_multiclass()

 CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for multiclass problems

Definition at line 259 of file Machine.cpp.

◆ apply_locked_regression()

 CRegressionLabels * apply_locked_regression ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for regression problems

Reimplemented in CKernelMachine.

Definition at line 252 of file Machine.cpp.

◆ apply_locked_structured()

 CStructuredLabels * apply_locked_structured ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for structured problems

Definition at line 266 of file Machine.cpp.

◆ apply_multiclass()

 CMulticlassLabels * 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

Reimplemented from CMachine.

Definition at line 101 of file CARTree.cpp.

◆ apply_one()

 virtual float64_t apply_one ( int32_t i )
virtualinherited

applies to one vector

Definition at line 257 of file Machine.h.

◆ apply_regression()

 CRegressionLabels * 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

Reimplemented from CMachine.

Definition at line 115 of file CARTree.cpp.

◆ apply_structured()

 CStructuredLabels * apply_structured ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 233 of file Machine.cpp.

 void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject *> * dict )
inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
 dict dictionary of parameters to be built.

Definition at line 635 of file SGObject.cpp.

◆ cancel_computation()

 SG_FORCED_INLINE bool cancel_computation ( ) const
inherited
Returns
whether the algorithm needs to be stopped

Definition at line 319 of file Machine.h.

◆ CARTtrain()

 CBinaryTreeMachineNode< CARTreeNodeData > * 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 316 of file CARTree.cpp.

◆ clear_feature_types()

 void clear_feature_types ( )

clear feature types of various features

Definition at line 201 of file CARTree.cpp.

◆ clear_weights()

 void clear_weights ( )

clear weights of data points

Definition at line 184 of file CARTree.cpp.

◆ clone()

 CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 734 of file SGObject.cpp.

◆ clone_parameters()

 bool clone_parameters ( CSGObject * other )
protectedinherited

Definition at line 759 of file SGObject.cpp.

◆ clone_tree()

 CTreeMachine* clone_tree ( )
inherited

clone tree

Returns
clone of entire tree

Definition at line 97 of file TreeMachine.h.

◆ compute_best_attribute()

 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, int32_t subset_size = 0, const SGVector< int32_t > & active_indices = SGVector() )
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

Reimplemented in CRandomCARTree.

Definition at line 530 of file CARTree.cpp.

◆ compute_error()

 float64_t 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 1328 of file CARTree.cpp.

◆ connect_to_signal_handler()

 rxcpp::subscription connect_to_signal_handler ( )
protectedinherited

connect the machine instance to the signal handler

Definition at line 280 of file Machine.cpp.

 void 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 1437 of file CARTree.cpp.

◆ data_lock()

 void data_lock ( CLabels * labs, CFeatures * features )
virtualinherited

Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called

Only possible if supports_locking() returns true

Parameters
 labs labels used for locking features features used for locking

Reimplemented in CKernelMachine.

Definition at line 119 of file Machine.cpp.

◆ data_unlock()

 void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 150 of file Machine.cpp.

◆ deep_copy()

 CSGObject * deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 232 of file SGObject.cpp.

◆ equals()

 bool equals ( CSGObject * other, float64_t accuracy = 0.0, bool tolerant = false )
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
 other object to compare with accuracy accuracy to use for comparison (optional) tolerant allows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 656 of file SGObject.cpp.

◆ find_weakest_alpha()

 float64_t 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 1416 of file CARTree.cpp.

◆ form_t1()

 void 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 1467 of file CARTree.cpp.

◆ gain() [1/2]

 float64_t 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 1052 of file CARTree.cpp.

◆ gain() [2/2]

 float64_t 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 1066 of file CARTree.cpp.

◆ get() [1/2]

 T get ( const Tag< T > & _tag ) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
 _tag name and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 381 of file SGObject.h.

◆ get() [2/2]

 T get ( const std::string & name ) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
 name name of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 404 of file SGObject.h.

◆ get_classifier_type()

 EMachineType get_classifier_type ( )
virtualinherited

get classifier type

Returns
classifier type NONE

Definition at line 99 of file Machine.cpp.

◆ get_feature_types()

 SGVector< bool > 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 196 of file CARTree.cpp.

◆ get_global_io()

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 269 of file SGObject.cpp.

◆ get_global_parallel()

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 311 of file SGObject.cpp.

◆ get_global_version()

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 324 of file SGObject.cpp.

◆ get_label_epsilon()

 float64_t get_label_epsilon ( )

get label epsilon

Returns
equality range for regression labels

Definition at line 223 of file CARTree.h.

◆ get_labels()

 CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 83 of file Machine.cpp.

◆ get_machine_problem_type()

 virtual EProblemType get_machine_problem_type ( ) const
virtual

get problem type - multiclass classification or regression

Returns
PT_MULTICLASS or PT_REGRESSION

Reimplemented from CBaseMulticlassMachine.

Definition at line 115 of file CARTree.h.

◆ get_max_depth()

 int32_t get_max_depth ( ) const

get max allowed tree depth

Returns
max allowed tree depth

Definition at line 218 of file CARTree.cpp.

◆ get_max_train_time()

 float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 94 of file Machine.cpp.

◆ get_min_node_size()

 int32_t get_min_node_size ( ) const

get min allowed node size

Returns
min allowed node size

Definition at line 229 of file CARTree.cpp.

◆ get_modelsel_names()

 SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 536 of file SGObject.cpp.

◆ get_modsel_param_descr()

 char * get_modsel_param_descr ( const char * param_name )
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 560 of file SGObject.cpp.

◆ get_modsel_param_index()

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

Parameters
 param_name name of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 573 of file SGObject.cpp.

◆ get_name()

 virtual const char* get_name ( ) const
virtual

get name

Returns
class name CARTree

Reimplemented from CTreeMachine< CARTreeNodeData >.

Reimplemented in CRandomCARTree.

Definition at line 110 of file CARTree.h.

◆ get_num_folds()

 int32_t get_num_folds ( ) const

get number of subsets used for cross validation

Returns
number of folds used in cross validation

Definition at line 207 of file CARTree.cpp.

◆ get_num_machines()

 int32_t get_num_machines ( ) const
inherited

get number of machines

Returns
number of machines

Definition at line 27 of file BaseMulticlassMachine.cpp.

◆ get_parameters_observable()

 SGObservable* get_parameters_observable ( )
inherited

Get parameters observable

Returns
RxCpp observable

Definition at line 415 of file SGObject.h.

◆ get_root()

 CTreeMachineNode* get_root ( )
inherited

get root

Returns
root the root node of the tree

Definition at line 88 of file TreeMachine.h.

◆ get_solver_type()

 ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 109 of file Machine.cpp.

◆ get_unique_labels()

 SGVector< float64_t > 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 506 of file CARTree.cpp.

◆ get_weights()

 SGVector< float64_t > get_weights ( ) const

get weights of data points

Returns
vector of weights

Definition at line 179 of file CARTree.cpp.

◆ gini_impurity_index()

 float64_t 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 1078 of file CARTree.cpp.

◆ handle_missing_vecs_for_continuous_surrogate()

 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 )
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 915 of file CARTree.cpp.

◆ handle_missing_vecs_for_nominal_surrogate()

 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 )
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 972 of file CARTree.cpp.

◆ has() [1/3]

 bool has ( const std::string & name ) const
inherited

Checks if object has a class parameter identified by a name.

Parameters
 name name of the parameter
Returns
true if the parameter exists with the input name

Definition at line 304 of file SGObject.h.

◆ has() [2/3]

 bool has ( const Tag< T > & tag ) const
inherited

Checks if object has a class parameter identified by a Tag.

Parameters
 tag tag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 315 of file SGObject.h.

◆ has() [3/3]

 bool has ( const std::string & name ) const
inherited

Checks if a type exists for a class parameter identified by a name.

Parameters
 name name of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 326 of file SGObject.h.

◆ init()

 void init ( )
protected

initializes members of class

Definition at line 1493 of file CARTree.cpp.

◆ is_data_locked()

 bool is_data_locked ( ) const
inherited
Returns
whether this machine is locked

Definition at line 308 of file Machine.h.

◆ is_generic()

 bool is_generic ( EPrimitiveType * generic ) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
 generic set to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 330 of file SGObject.cpp.

◆ is_label_valid()

 bool 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

Reimplemented from CBaseMulticlassMachine.

Definition at line 91 of file CARTree.cpp.

◆ least_squares_deviation()

 float64_t 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 1088 of file CARTree.cpp.

◆ list_observable_parameters()

 void list_observable_parameters ( )
inherited

Print to stdout a list of observable parameters

Definition at line 878 of file SGObject.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "" )
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
 file where to load from prefix prefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 403 of file SGObject.cpp.

 void load_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 460 of file SGObject.cpp.

 void load_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 455 of file SGObject.cpp.

◆ observe()

 void observe ( const ObservedValue value )
protectedinherited

Observe a parameter value and emit them to observer.

Parameters
 value Observed parameter's value

Definition at line 828 of file SGObject.cpp.

◆ on_complete()

 virtual void on_complete ( )
protectedvirtualinherited

The action which will be done when the user decides to return to prompt and terminate the program execution

Definition at line 427 of file Machine.h.

◆ on_next()

 virtual void on_next ( )
protectedvirtualinherited

The action which will be done when the user decides to premature stop the CMachine execution

Definition at line 411 of file Machine.h.

◆ on_pause()

 virtual void on_pause ( )
protectedvirtualinherited

The action which will be done when the user decides to pause the CMachine execution

Definition at line 418 of file Machine.h.

◆ parameter_hash_changed()

 bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 296 of file SGObject.cpp.

◆ pause_computation()

 SG_FORCED_INLINE void pause_computation ( )
inherited

Pause the algorithm if the flag is set

Definition at line 327 of file Machine.h.

◆ post_lock()

 virtual void post_lock ( CLabels * labs, CFeatures * features )
virtualinherited

post lock

Definition at line 299 of file Machine.h.

◆ pre_sort_features()

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

Definition at line 296 of file CARTree.cpp.

◆ print_modsel_params()

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 512 of file SGObject.cpp.

◆ print_serializable()

 void print_serializable ( const char * prefix = "" )
virtualinherited

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 342 of file SGObject.cpp.

◆ prune_by_cross_validation()

 void 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 1189 of file CARTree.cpp.

◆ prune_tree()

 CDynamicObjectArray * prune_tree ( CTreeMachine< CARTreeNodeData > * tree )
protected

cost-complexity pruning

Parameters
 tree the tree to be pruned
Returns
CDynamicObjectArray of pruned trees

Definition at line 1366 of file CARTree.cpp.

◆ prune_using_test_dataset()

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

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 127 of file CARTree.cpp.

◆ ref()

 int32_t ref ( )
inherited

increase reference counter

Returns
reference count

Definition at line 186 of file SGObject.cpp.

◆ ref_count()

 int32_t ref_count ( )
inherited

display reference counter

Returns
reference count

Definition at line 193 of file SGObject.cpp.

◆ register_observable_param()

 void register_observable_param ( const std::string & name, const SG_OBS_VALUE_TYPE type, const std::string & description )
protectedinherited

Register which params this object can emit.

Parameters
 name the param name type the param type description a user oriented description

Definition at line 871 of file SGObject.cpp.

◆ register_param() [1/2]

 void register_param ( Tag< T > & _tag, const T & value )
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
 _tag name and type information of parameter value value of the parameter

Definition at line 472 of file SGObject.h.

◆ register_param() [2/2]

 void register_param ( const std::string & name, const T & value )
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
 name name of the parameter value value of the parameter along with type information

Definition at line 485 of file SGObject.h.

◆ reset_computation_variables()

 void reset_computation_variables ( )
protectedinherited

reset the computation variables

Definition at line 403 of file Machine.h.

◆ resume_computation()

 SG_FORCED_INLINE void resume_computation ( )
inherited

Resume current computation (sets the flag)

Definition at line 340 of file Machine.h.

◆ save_serializable()

 bool save_serializable ( CSerializableFile * file, const char * prefix = "" )
virtualinherited

Save this object to file.

Parameters
 file where to save the object; will be closed during returning if PREFIX is an empty string. prefix prefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 348 of file SGObject.cpp.

◆ save_serializable_post()

 void save_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 470 of file SGObject.cpp.

◆ save_serializable_pre()

 void save_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 465 of file SGObject.cpp.

◆ set() [1/2]

 void set ( const Tag< T > & _tag, const T & value )
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
 _tag name and type information of parameter value value of the parameter

Definition at line 342 of file SGObject.h.

◆ set() [2/2]

 void set ( const std::string & name, const T & value )
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
 name name of the parameter value value of the parameter along with type information

Definition at line 368 of file SGObject.h.

◆ set_cv_pruning()

 void set_cv_pruning ( bool cv_pruning )

Set cross validation pruning parameter

Parameters
 cv_pruning allow CV pruning

Definition at line 214 of file CARTree.h.

◆ set_feature_types()

 void 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 190 of file CARTree.cpp.

◆ set_generic() [1/16]

 void set_generic ( )
inherited

Definition at line 73 of file SGObject.cpp.

◆ set_generic() [2/16]

 void set_generic ( )
inherited

Definition at line 78 of file SGObject.cpp.

◆ set_generic() [3/16]

 void set_generic ( )
inherited

Definition at line 83 of file SGObject.cpp.

◆ set_generic() [4/16]

 void set_generic ( )
inherited

Definition at line 88 of file SGObject.cpp.

◆ set_generic() [5/16]

 void set_generic ( )
inherited

Definition at line 93 of file SGObject.cpp.

◆ set_generic() [6/16]

 void set_generic ( )
inherited

Definition at line 98 of file SGObject.cpp.

◆ set_generic() [7/16]

 void set_generic ( )
inherited

Definition at line 103 of file SGObject.cpp.

◆ set_generic() [8/16]

 void set_generic ( )
inherited

Definition at line 108 of file SGObject.cpp.

◆ set_generic() [9/16]

 void set_generic ( )
inherited

Definition at line 113 of file SGObject.cpp.

◆ set_generic() [10/16]

 void set_generic ( )
inherited

Definition at line 118 of file SGObject.cpp.

◆ set_generic() [11/16]

 void set_generic ( )
inherited

Definition at line 123 of file SGObject.cpp.

◆ set_generic() [12/16]

 void set_generic ( )
inherited

Definition at line 128 of file SGObject.cpp.

◆ set_generic() [13/16]

 void set_generic ( )
inherited

Definition at line 133 of file SGObject.cpp.

◆ set_generic() [14/16]

 void set_generic ( )
inherited

Definition at line 138 of file SGObject.cpp.

◆ set_generic() [15/16]

 void set_generic ( )
inherited

Definition at line 143 of file SGObject.cpp.

◆ set_generic() [16/16]

 void set_generic ( )
inherited

set generic type to T

◆ set_global_io()

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 262 of file SGObject.cpp.

◆ set_global_parallel()

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 275 of file SGObject.cpp.

◆ set_global_version()

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 317 of file SGObject.cpp.

◆ set_label_epsilon()

 void set_label_epsilon ( float64_t epsilon )

set label epsilon

Parameters
 epsilon equality range for regression labels

Definition at line 240 of file CARTree.cpp.

◆ set_labels()

 void set_labels ( CLabels * lab )
virtual

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

Parameters
 lab labels

Reimplemented from CMachine.

Definition at line 72 of file CARTree.cpp.

◆ set_machine_problem_type()

 void set_machine_problem_type ( EProblemType mode )

set problem type - multiclass classification or regression

Parameters
 mode EProblemType PT_MULTICLASS or PT_REGRESSION

Definition at line 86 of file CARTree.cpp.

◆ set_max_depth()

 void set_max_depth ( int32_t depth )

set max allowed tree depth

Parameters
 depth max allowed tree depth

Definition at line 223 of file CARTree.cpp.

◆ set_max_train_time()

 void set_max_train_time ( float64_t t )
inherited

set maximum training time

Parameters
 t maximimum training time

Definition at line 89 of file Machine.cpp.

◆ set_min_node_size()

 void set_min_node_size ( int32_t nsize )

set min allowed node size

Parameters
 nsize min allowed node size

Definition at line 234 of file CARTree.cpp.

◆ set_num_folds()

 void 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 212 of file CARTree.cpp.

◆ set_root()

 void set_root ( CTreeMachineNode< CARTreeNodeData > * root )
inherited

set root

Parameters
 root the root node of the tree

Definition at line 78 of file TreeMachine.h.

◆ set_solver_type()

 void set_solver_type ( ESolverType st )
inherited

set solver type

Parameters
 st solver type

Definition at line 104 of file Machine.cpp.

◆ set_sorted_features()

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

Definition at line 289 of file CARTree.cpp.

◆ set_store_model_features()

 void set_store_model_features ( bool store_model )
virtualinherited

Setter for store-model-features-after-training flag

Parameters
 store_model whether model should be stored after training

Definition at line 114 of file Machine.cpp.

◆ set_weights()

 void set_weights ( SGVector< float64_t > w )

set weights of data points

Parameters
 w vector of weights

Definition at line 173 of file CARTree.cpp.

◆ shallow_copy()

 CSGObject * shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 226 of file SGObject.cpp.

◆ store_model_features()

 virtual void store_model_features ( )
protectedvirtualinherited

enable unlocked cross-validation - no model features to store

Reimplemented from CMachine.

Definition at line 152 of file TreeMachine.h.

◆ subscribe_to_parameters()

 void subscribe_to_parameters ( ParameterObserverInterface * obs )
inherited

Subscribe a parameter observer to watch over params

Definition at line 811 of file SGObject.cpp.

◆ supports_locking()

 virtual bool supports_locking ( ) const
virtualinherited
Returns
whether this machine supports locking

Reimplemented in CKernelMachine.

Definition at line 305 of file Machine.h.

◆ surrogate_split()

 SGVector< bool > 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 840 of file CARTree.cpp.

◆ train()

 bool train ( CFeatures * data = NULL )
virtualinherited

train machine

Parameters
 data training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training.
Returns
whether training was successful

Reimplemented in CRelaxedTree, CAutoencoder, CLinearMachine, CSGDQN, and COnlineSVMSGD.

Definition at line 43 of file Machine.cpp.

◆ train_locked()

 virtual bool train_locked ( SGVector< index_t > indices )
virtualinherited

Trains a locked machine on a set of indices. Error if machine is not locked

NOT IMPLEMENTED

Parameters
 indices index vector (of locked features) that is used for training
Returns
whether training was successful

Reimplemented in CKernelMachine.

Definition at line 248 of file Machine.h.

◆ train_machine()

 bool train_machine ( CFeatures * data = NULL )
protectedvirtual

train machine - build CART from training data

Parameters
 data training data
Returns
true

Reimplemented from CMachine.

Definition at line 246 of file CARTree.cpp.

◆ train_require_labels()

 virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Definition at line 397 of file Machine.h.

◆ unref()

 int32_t unref ( )
inherited

decrement reference counter and deallocate object if refcount is zero before or after decrementing it

Returns
reference count

Definition at line 200 of file SGObject.cpp.

◆ unset_generic()

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 337 of file SGObject.cpp.

◆ update_parameter_hash()

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 282 of file SGObject.cpp.

◆ EQ_DELTA

 const float64_t EQ_DELTA =1e-7
static

equality epsilon

Definition at line 422 of file CARTree.h.

◆ io

 SGIO* io
inherited

io

Definition at line 600 of file SGObject.h.

◆ m_alphas

 CDynamicArray* m_alphas
protected

stores $$\alpha_k$$ values evaluated in cost-complexity pruning

Definition at line 459 of file CARTree.h.

◆ m_apply_cv_pruning

 bool m_apply_cv_pruning
protected

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

Definition at line 450 of file CARTree.h.

◆ m_cancel_computation

 std::atomic m_cancel_computation
protectedinherited

Cancel computation

Definition at line 448 of file Machine.h.

◆ m_data_locked

 bool m_data_locked
protectedinherited

whether data is locked

Definition at line 445 of file Machine.h.

◆ m_folds

 int32_t m_folds
protected

V in V-fold cross validation - 5 by default

Definition at line 453 of file CARTree.h.

inherited

parameters wrt which we can compute gradients

Definition at line 615 of file SGObject.h.

◆ m_hash

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 618 of file SGObject.h.

◆ m_label_epsilon

 float64_t m_label_epsilon
protected

equality range for regression labels

Definition at line 426 of file CARTree.h.

◆ m_labels

 CLabels* m_labels
protectedinherited

labels

Definition at line 436 of file Machine.h.

◆ m_machines

 CDynamicObjectArray* m_machines
protectedinherited

machines

Definition at line 56 of file BaseMulticlassMachine.h.

◆ m_max_depth

 int32_t m_max_depth
protected

max allowed depth of tree

Definition at line 462 of file CARTree.h.

◆ m_max_train_time

 float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 433 of file Machine.h.

◆ m_min_node_size

 int32_t m_min_node_size
protected

minimum number of feature vectors required in a node

Definition at line 465 of file CARTree.h.

◆ m_mode

 EProblemType m_mode
protected

Problem type : PT_MULTICLASS or PT_REGRESSION

Definition at line 456 of file CARTree.h.

◆ m_model_selection_parameters

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 612 of file SGObject.h.

◆ m_mutex

 std::mutex m_mutex
protectedinherited

Definition at line 457 of file Machine.h.

◆ m_nominal

 SGVector m_nominal
protected

vector depicting whether various feature dimensions are nominal or not

Definition at line 429 of file CARTree.h.

◆ m_parameters

 Parameter* m_parameters
inherited

parameters

Definition at line 609 of file SGObject.h.

◆ m_pause_computation

 std::condition_variable m_pause_computation
protectedinherited

Conditional variable to make threads wait

Definition at line 454 of file Machine.h.

◆ m_pause_computation_flag

 std::atomic m_pause_computation_flag
protectedinherited

Pause computation flag

Definition at line 451 of file Machine.h.

◆ m_pre_sort

 bool m_pre_sort
protected

If pre sorted features are used in train

Definition at line 441 of file CARTree.h.

◆ m_root

 CTreeMachineNode* m_root
protectedinherited

tree root

Definition at line 156 of file TreeMachine.h.

◆ m_solver_type

 ESolverType m_solver_type
protectedinherited

solver type

Definition at line 439 of file Machine.h.

◆ m_sorted_features

 SGMatrix m_sorted_features
protected

sorted transposed features

Definition at line 435 of file CARTree.h.

◆ m_sorted_indices

 SGMatrix m_sorted_indices
protected

sorted indices

Definition at line 438 of file CARTree.h.

◆ m_store_model_features

 bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 442 of file Machine.h.

◆ m_types_set

 bool m_types_set
protected

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

Definition at line 444 of file CARTree.h.

◆ m_weights

 SGVector m_weights
protected

weights of samples in training set

Definition at line 432 of file CARTree.h.

◆ m_weights_set

 bool m_weights_set
protected

flag storing whether weights of samples are specified using weights vector

Definition at line 447 of file CARTree.h.

◆ MIN_SPLIT_GAIN

 const float64_t MIN_SPLIT_GAIN =1e-7
static

min gain for splitting to be allowed

Definition at line 419 of file CARTree.h.

◆ MISSING

 const float64_t MISSING =CMath::MAX_REAL_NUMBER
static

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

Definition at line 416 of file CARTree.h.

◆ parallel

 Parallel* parallel
inherited

parallel

Definition at line 603 of file SGObject.h.

◆ version

 Version* version
inherited

version

Definition at line 606 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation