ML::DT Namespace Reference

Classes
struct	DecisionTreeParams
struct	TreeMetaDataNode
struct	Dataset
struct	Quantiles
class	TreeliteType
class	TreeliteType< float >
class	TreeliteType< double >
class	TreeliteType< uint32_t >
class	TreeliteType< int >

Typedefs
typedef TreeMetaDataNode< float, int >	TreeClassifierF
typedef TreeMetaDataNode< double, int >	TreeClassifierD
typedef TreeMetaDataNode< float, float >	TreeRegressorF
typedef TreeMetaDataNode< double, double >	TreeRegressorD

Functions
void	set_tree_params (DecisionTreeParams &params, int cfg_max_depth=-1, int cfg_max_leaves=-1, float cfg_max_features=1.0f, int cfg_max_n_bins=128, int cfg_min_samples_leaf=1, int cfg_min_samples_split=2, float cfg_min_impurity_decrease=0.0f, CRITERION cfg_split_criterion=CRITERION_END, int cfg_max_batch_size=4096)
	Set all DecisionTreeParams members. More...
template<class T , class L >
std::string	get_tree_summary_text (const TreeMetaDataNode< T, L > *tree)
	Obtain high-level tree information. More...
template<class T , class L >
std::string	get_tree_text (const TreeMetaDataNode< T, L > *tree)
	Obtain detailed tree information. More...
template<class T , class L >
std::string	get_tree_json (const TreeMetaDataNode< T, L > *tree)
	Export tree as a JSON string. More...

Typedef Documentation

TreeClassifierD

typedef TreeMetaDataNode<double, int> ML::DT::TreeClassifierD

TreeClassifierF

typedef TreeMetaDataNode<float, int> ML::DT::TreeClassifierF

TreeRegressorD

typedef TreeMetaDataNode<double, double> ML::DT::TreeRegressorD

TreeRegressorF

typedef TreeMetaDataNode<float, float> ML::DT::TreeRegressorF

Function Documentation

get_tree_json()

template<class T , class L >

std::string ML::DT::get_tree_json

(

const TreeMetaDataNode< T, L > *

tree

)

Export tree as a JSON string.

Template Parameters

T	data type for input data (float or double).
L	data type for labels (int type for classification, T type for regression).

Parameters

[in]

tree

CPU pointer to TreeMetaDataNode

Returns

Tree structure as JSON stsring

get_tree_summary_text()

template<class T , class L >

std::string ML::DT::get_tree_summary_text

(

const TreeMetaDataNode< T, L > *

tree

)

Obtain high-level tree information.

Template Parameters

T	data type for input data (float or double).
L	data type for labels (int type for classification, T type for regression).

Parameters

[in]

tree

CPU pointer to TreeMetaDataNode

Returns

High-level tree information as string

get_tree_text()

template<class T , class L >

std::string ML::DT::get_tree_text

(

const TreeMetaDataNode< T, L > *

tree

)

Obtain detailed tree information.

Template Parameters

T	data type for input data (float or double).
L	data type for labels (int type for classification, T type for regression).

Parameters

[in]

tree

CPU pointer to TreeMetaDataNode

Returns

Detailed tree information as string

set_tree_params()

void ML::DT::set_tree_params	(	DecisionTreeParams &	params,
		int	cfg_max_depth = -1,
		int	cfg_max_leaves = -1,
		float	cfg_max_features = 1.0f,
		int	cfg_max_n_bins = 128,
		int	cfg_min_samples_leaf = 1,
		int	cfg_min_samples_split = 2,
		float	cfg_min_impurity_decrease = 0.0f,
		CRITERION	cfg_split_criterion = CRITERION_END,
		int	cfg_max_batch_size = 4096
	)

Set all DecisionTreeParams members.

Parameters

[in,out]	params	update with tree parameters
[in]	cfg_max_depth	maximum tree depth; default -1
[in]	cfg_max_leaves	maximum leaves; default -1
[in]	cfg_max_features	maximum number of features; default 1.0f
[in]	cfg_max_n_bins	maximum number of bins; default 128
[in]	cfg_min_samples_leaf	min. rows in each leaf node; default 1
[in]	cfg_min_samples_split	min. rows needed to split an internal node; default 2
[in]	cfg_min_impurity_decrease	split a node only if its reduction in impurity is more than this value
[in]	cfg_split_criterion	split criterion; default CRITERION_END, i.e., GINI for classification or MSE for regression
[in]	cfg_max_batch_size	Maximum number of nodes that can be processed in a batch. This is used only for batched-level algo. Default value 4096.