Decision tree data structure. More...

#include <tree.h>

Public Member Functions
void	CloneFromTree (Tree *tree)
	Copy the structure and parameters of another tree. If the `Tree` object calling this method already has a non-root tree structure / parameters, this will be erased and replaced with a copy of `tree`.

void	Reset ()
	Reset tree to empty vectors and default values of boolean / integer variables.

void	Init (int output_dimension=1, bool is_log_scale=false)
	Initialize the tree with a single root node.

int	AllocNode ()
	Allocate a new node and return the node's ID.

void	DeleteNode (std::int32_t nid)
	Deletes node indexed by node ID.

void	ExpandNode (std::int32_t nid, int split_index, double split_value, double left_value, double right_value)
	Expand a node based on a numeric split rule.

void	ExpandNode (std::int32_t nid, int split_index, std::vector< std::uint32_t > const &categorical_indices, double left_value, double right_value)
	Expand a node based on a categorical split rule.

void	ExpandNode (std::int32_t nid, int split_index, double split_value, std::vector< double > left_value_vector, std::vector< double > right_value_vector)
	Expand a node based on a numeric split rule.

void	ExpandNode (std::int32_t nid, int split_index, std::vector< std::uint32_t > const &categorical_indices, std::vector< double > left_value_vector, std::vector< double > right_value_vector)
	Expand a node based on a categorical split rule.

void	ExpandNode (std::int32_t nid, int split_index, TreeSplit &split, double left_value, double right_value)
	Expand a node based on a generic split rule.

void	ExpandNode (std::int32_t nid, int split_index, TreeSplit &split, std::vector< double > left_value_vector, std::vector< double > right_value_vector)
	Expand a node based on a generic split rule.

bool	IsRoot ()
	Whether or not a tree is a "stump" consisting of a single root node.

json	to_json ()
	Convert tree to JSON and return JSON in-memory.

void	from_json (const json &tree_json)
	Load from JSON.

void	CollapseToLeaf (std::int32_t nid, double value)
	Collapse an internal node to a leaf node, deleting its children from the tree.

void	CollapseToLeaf (std::int32_t nid, std::vector< double > value_vector)
	Collapse an internal node to a leaf node, deleting its children from the tree.

template<typename Func >
void	WalkTree (Func func) const
	Iterate through all nodes in this tree.

bool	HasVectorOutput () const
	Whether or not a tree has vector output.

std::int32_t	OutputDimension () const
	Dimension of tree output.

bool	IsLogScale () const
	Whether or not tree parameters should be exponentiated at prediction time.

std::int32_t	Parent (std::int32_t nid) const
	Index of the node's parent.

std::int32_t	LeftChild (std::int32_t nid) const
	Index of the node's left child.

std::int32_t	RightChild (std::int32_t nid) const
	Index of the node's right child.

std::int32_t	DefaultChild (std::int32_t nid) const
	Index of the node's "default" child (potentially used in the case of a missing feature at prediction time)

std::int32_t	SplitIndex (std::int32_t nid) const
	Feature index defining the node's split rule.

bool	IsLeaf (std::int32_t nid) const
	Whether the node is a leaf node.

bool	IsRoot (std::int32_t nid) const
	Whether the node is root.

bool	IsDeleted (std::int32_t nid) const
	Whether the node has been deleted.

double	LeafValue (std::int32_t nid) const
	Get parameter value of a node (typically though not necessarily a leaf node)

double	LeafValue (std::int32_t nid, std::int32_t dim_id) const
	Get parameter value of a node (typically though not necessarily a leaf node) at a given output dimension.

std::int32_t	MaxLeafDepth () const
	Get maximum depth of all of the leaf nodes.

std::vector< double >	LeafVector (std::int32_t nid) const
	Get vector-valued parameters of a node (typically leaf)

double	SumSquaredNodeValues (std::int32_t nid) const
	Sum of squared parameter values for a given node (typically though not necessarily a leaf node)

double	SumSquaredLeafValues () const
	Sum of squared values for all leaves in a tree.

bool	HasLeafVector (std::int32_t nid) const
	Tests whether the leaf node has a non-empty leaf vector.

double	Threshold (std::int32_t nid) const
	Get split threshold of the node.

std::vector< std::uint32_t >	CategoryList (std::int32_t nid) const
	Get list of all categories belonging to the left child node. Categories are integers ranging from 0 to (n-1), where n is the number of categories in that particular feature. This list is assumed to be in ascending order.

TreeNodeType	NodeType (std::int32_t nid) const
	Get the type of a node (i.e. numeric split, categorical split, leaf)

bool	IsNumericSplitNode (std::int32_t nid) const
	Whether the node is a numeric split node.

bool	IsCategoricalSplitNode (std::int32_t nid) const
	Whether the node is a numeric split node.

bool	HasCategoricalSplit () const
	Query whether this tree contains any categorical splits.

std::vector< std::int32_t > const &	GetInternalNodes () const
	Get indices of all internal nodes.

std::vector< std::int32_t > const &	GetLeaves () const
	Get indices of all leaf nodes.

std::vector< std::int32_t > const &	GetLeafParents () const
	Get indices of all leaf parent nodes.

std::vector< std::int32_t >	GetNodes ()
	Get indices of all valid (non-deleted) nodes.

std::int32_t	GetDepth (std::int32_t nid) const
	Get the depth of a node.

std::int32_t	NumNodes () const noexcept
	Get the total number of nodes including deleted ones in this tree.

std::int32_t	NumDeletedNodes () const noexcept
	Get the total number of deleted nodes in this tree.

std::int32_t	NumValidNodes () const noexcept
	Get the total number of valid nodes in this tree.

void	SetLeftChild (std::int32_t nid, std::int32_t left_child)
	Identify left child node.

void	SetRightChild (std::int32_t nid, std::int32_t right_child)
	Identify right child node.

void	SetChildren (std::int32_t nid, std::int32_t left_child, std::int32_t right_child)
	Identify two child nodes of the node and the corresponding parent node of the child nodes.

void	SetParent (std::int32_t child_node, std::int32_t parent_node)
	Identify parent node.

void	SetParents (std::int32_t nid, std::int32_t left_child, std::int32_t right_child)
	Identify parent node of the left and right node ids.

void	SetNumericSplit (std::int32_t nid, std::int32_t split_index, double threshold)
	Create a numerical split.

void	SetCategoricalSplit (std::int32_t nid, std::int32_t split_index, std::vector< std::uint32_t > const &category_list)
	Create a categorical split.

void	SetLeaf (std::int32_t nid, double value)
	Set the leaf value of the node.

void	SetLeafVector (std::int32_t nid, std::vector< double > const &leaf_vector)
	Set the leaf vector of the node; useful for multi-output trees.

void	PredictLeafIndexInplace (ForestDataset *dataset, std::vector< int32_t > &output, int32_t offset, int32_t max_leaf)
	Obtain a 0-based leaf index for each observation in a ForestDataset. Internally, trees are stored as vectors of node information, and the `leaves_` vector gives us node IDs for every leaf in the tree. Here, we would like to know, for every observation in a dataset, which leaf number it is mapped to. Since the leaf numbers themselves do not carry any information, we renumber them from `0` to `leaves_.size()-1`.

void	PredictLeafIndexInplace (Eigen::MatrixXd &covariates, std::vector< int32_t > &output, int32_t offset, int32_t max_leaf)
	Obtain a 0-based leaf index for each observation in a ForestDataset. Internally, trees are stored as vectors of node information, and the `leaves_` vector gives us node IDs for every leaf in the tree. Here, we would like to know, for every observation in a dataset, which leaf number it is mapped to. Since the leaf numbers themselves do not carry any information, we renumber them from `0` to `leaves_.size()-1`.

void	PredictLeafIndexInplace (Eigen::Map< Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor > > &covariates, std::vector< int32_t > &output, int32_t offset, int32_t max_leaf)
	Obtain a 0-based leaf index for each observation in a ForestDataset. Internally, trees are stored as vectors of node information, and the `leaves_` vector gives us node IDs for every leaf in the tree. Here, we would like to know, for every observation in a dataset, which leaf number it is mapped to. Since the leaf numbers themselves do not carry any information, we renumber them from `0` to `leaves_.size()-1`.

Detailed Description

Decision tree data structure.

Member Function Documentation

◆ CloneFromTree()

void StochTree::Tree::CloneFromTree ( Tree * tree )

Copy the structure and parameters of another tree. If the Tree object calling this method already has a non-root tree structure / parameters, this will be erased and replaced with a copy of tree.

Parameters

tree	Tree to be cloned

◆ from_json()

void StochTree::Tree::from_json ( const json & tree_json )

Load from JSON.

Parameters

tree_json In-memory json object (of type nlohmann::json)

◆ CollapseToLeaf() [1/2]

void StochTree::Tree::CollapseToLeaf	(	std::int32_t	nid,
		double	value
	)

inline

Collapse an internal node to a leaf node, deleting its children from the tree.

Parameters

nid	Node id of the new leaf node
value_vector	New leaf value

◆ CollapseToLeaf() [2/2]

void StochTree::Tree::CollapseToLeaf	(	std::int32_t	nid,
		std::vector< double >	value_vector
	)

inline

Collapse an internal node to a leaf node, deleting its children from the tree.

Parameters

nid	Node id of the new leaf node
value_vector	New leaf vector value

◆ WalkTree()

template<typename Func >

void StochTree::Tree::WalkTree ( Func func ) const

inline

Iterate through all nodes in this tree.

Template Parameters

Func	Function object type, must map `std::int32_t` to `bool`.

Parameters

func	Function that accepts a node index and returns `False` when iteration through a given branch of the tree should stop and `True` otherwise.

◆ HasVectorOutput()

bool StochTree::Tree::HasVectorOutput ( ) const

inline

Whether or not a tree has vector output.

Getters

◆ Parent()

std::int32_t StochTree::Tree::Parent ( std::int32_t nid ) const

inline

Index of the node's parent.

Parameters

nid	ID of node being queried

◆ LeftChild()

std::int32_t StochTree::Tree::LeftChild ( std::int32_t nid ) const

inline

Index of the node's left child.

Parameters

nid	ID of node being queried

◆ RightChild()

std::int32_t StochTree::Tree::RightChild ( std::int32_t nid ) const

inline

Index of the node's right child.

Parameters

nid	ID of node being queried

◆ DefaultChild()

std::int32_t StochTree::Tree::DefaultChild ( std::int32_t nid ) const

inline

Index of the node's "default" child (potentially used in the case of a missing feature at prediction time)

Parameters

nid	ID of node being queried

◆ SplitIndex()

std::int32_t StochTree::Tree::SplitIndex ( std::int32_t nid ) const

inline

Feature index defining the node's split rule.

Parameters

nid	ID of node being queried

◆ IsLeaf()

bool StochTree::Tree::IsLeaf ( std::int32_t nid ) const

inline

Whether the node is a leaf node.

Parameters

nid	ID of node being queried

◆ IsRoot()

bool StochTree::Tree::IsRoot ( std::int32_t nid ) const

inline

Whether the node is root.

Parameters

nid	ID of node being queried

◆ IsDeleted()

bool StochTree::Tree::IsDeleted ( std::int32_t nid ) const

inline

Whether the node has been deleted.

Parameters

nid	ID of node being queried

◆ LeafValue() [1/2]

double StochTree::Tree::LeafValue ( std::int32_t nid ) const

inline

Get parameter value of a node (typically though not necessarily a leaf node)

Parameters

nid	ID of node being queried

◆ LeafValue() [2/2]

double StochTree::Tree::LeafValue	(	std::int32_t	nid,
		std::int32_t	dim_id
	)		const

inline

Get parameter value of a node (typically though not necessarily a leaf node) at a given output dimension.

Parameters

nid	ID of node being queried
dim_id	Output dimension being queried

◆ LeafVector()

std::vector< double > StochTree::Tree::LeafVector ( std::int32_t nid ) const

inline

Get vector-valued parameters of a node (typically leaf)

Parameters

nid	ID of node being queried

◆ SumSquaredNodeValues()

double StochTree::Tree::SumSquaredNodeValues ( std::int32_t nid ) const

inline

Sum of squared parameter values for a given node (typically though not necessarily a leaf node)

Parameters

nid	ID of node being queried

◆ HasLeafVector()

bool StochTree::Tree::HasLeafVector ( std::int32_t nid ) const

inline

Tests whether the leaf node has a non-empty leaf vector.

Parameters

nid	ID of node being queried

◆ Threshold()

double StochTree::Tree::Threshold ( std::int32_t nid ) const

inline

Get split threshold of the node.

Parameters

nid	ID of node being queried

◆ CategoryList()

std::vector< std::uint32_t > StochTree::Tree::CategoryList ( std::int32_t nid ) const

inline

Get list of all categories belonging to the left child node. Categories are integers ranging from 0 to (n-1), where n is the number of categories in that particular feature. This list is assumed to be in ascending order.

Parameters

nid	ID of node being queried

◆ NodeType()

TreeNodeType StochTree::Tree::NodeType ( std::int32_t nid ) const

inline

Get the type of a node (i.e. numeric split, categorical split, leaf)

Parameters

nid	ID of node being queried

◆ IsNumericSplitNode()

bool StochTree::Tree::IsNumericSplitNode ( std::int32_t nid ) const

inline

Whether the node is a numeric split node.

Parameters

nid	ID of node being queried

◆ IsCategoricalSplitNode()

bool StochTree::Tree::IsCategoricalSplitNode ( std::int32_t nid ) const

inline

Whether the node is a numeric split node.

Parameters

nid	ID of node being queried

◆ GetDepth()

std::int32_t StochTree::Tree::GetDepth ( std::int32_t nid ) const

inline

Get the depth of a node.

Parameters

nid node id

◆ SetLeftChild()

void StochTree::Tree::SetLeftChild	(	std::int32_t	nid,
		std::int32_t	left_child
	)

inline

Identify left child node.

Setters

Parameters

nid	ID of node being modified
left_child	ID of the left child node

◆ SetRightChild()

void StochTree::Tree::SetRightChild	(	std::int32_t	nid,
		std::int32_t	right_child
	)

inline

Identify right child node.

Parameters

nid	ID of node being modified
right_child	ID of the right child node

◆ SetChildren()

void StochTree::Tree::SetChildren	(	std::int32_t	nid,
		std::int32_t	left_child,
		std::int32_t	right_child
	)

inline

Identify two child nodes of the node and the corresponding parent node of the child nodes.

Parameters

nid	ID of node being modified
left_child	ID of the left child node
right_child	ID of the right child node

◆ SetParent()

void StochTree::Tree::SetParent	(	std::int32_t	child_node,
		std::int32_t	parent_node
	)

inline

Identify parent node.

Parameters

child_node	ID of child node
parent_node	ID of the parent node

◆ SetParents()

void StochTree::Tree::SetParents	(	std::int32_t	nid,
		std::int32_t	left_child,
		std::int32_t	right_child
	)

inline

Identify parent node of the left and right node ids.

Parameters

nid	ID of parent node
left_child	ID of the left child node
right_child	ID of the right child node

◆ SetNumericSplit()

void StochTree::Tree::SetNumericSplit	(	std::int32_t	nid,
		std::int32_t	split_index,
		double	threshold
	)

Create a numerical split.

Parameters

nid	ID of node being updated
split_index	Feature index to split
threshold	Threshold value

◆ SetCategoricalSplit()

void StochTree::Tree::SetCategoricalSplit	(	std::int32_t	nid,
		std::int32_t	split_index,
		std::vector< std::uint32_t > const &	category_list
	)

Create a categorical split.

Parameters

nid	ID of node being updated
split_index	Feature index to split
category_list	List of categories to belong to either the right child node or the left child node. Set categories_list_right_child parameter to indicate which node the category list should represent.

◆ SetLeaf()

void StochTree::Tree::SetLeaf	(	std::int32_t	nid,
		double	value
	)

Set the leaf value of the node.

Parameters

nid	ID of node being updated
value	Leaf value

◆ SetLeafVector()

void StochTree::Tree::SetLeafVector	(	std::int32_t	nid,
		std::vector< double > const &	leaf_vector
	)

Set the leaf vector of the node; useful for multi-output trees.

Parameters

nid	ID of node being updated
leaf_vector	Leaf vector

◆ PredictLeafIndexInplace() [1/3]

void StochTree::Tree::PredictLeafIndexInplace	(	ForestDataset *	dataset,
		std::vector< int32_t > &	output,
		int32_t	offset,
		int32_t	max_leaf
	)

Obtain a 0-based leaf index for each observation in a ForestDataset. Internally, trees are stored as vectors of node information, and the leaves_ vector gives us node IDs for every leaf in the tree. Here, we would like to know, for every observation in a dataset, which leaf number it is mapped to. Since the leaf numbers themselves do not carry any information, we renumber them from 0 to leaves_.size()-1.

Note: this is a tree-level helper function for an ensemble-level function. It assumes the creation of:

a vector of column indices of size dataset.NumObservations() x ensemble.NumTrees(), stored in "tree-major" order

a running counter of the number of tree-observations already indexed in the ensemble
(used as offsets for the leaf number computed and returned here) Users running this function for a single tree may simply pre-allocate an output vector as std::vector<int32_t> output(dataset->NumObservations()) and set the offset to 0.

Parameters

dataset	Dataset with which to predict leaf indices from the tree
output	Pre-allocated output vector storing a matrix of column indices, with "rows" corresponding to observations in `dataset` and "columns" corresponding to trees in an ensemble
offset	Bookkeeping index that determines where in `output` vector that column indices should be unpacked
max_leaf	Largest leaf value mapped so far. (Leaf indices serve as sparse column indices, so it is important that leaf values be unique to each tree.)

◆ PredictLeafIndexInplace() [2/3]

void StochTree::Tree::PredictLeafIndexInplace	(	Eigen::MatrixXd &	covariates,
		std::vector< int32_t > &	output,
		int32_t	offset,
		int32_t	max_leaf
	)

Obtain a 0-based leaf index for each observation in a ForestDataset. Internally, trees are stored as vectors of node information, and the leaves_ vector gives us node IDs for every leaf in the tree. Here, we would like to know, for every observation in a dataset, which leaf number it is mapped to. Since the leaf numbers themselves do not carry any information, we renumber them from 0 to leaves_.size()-1.

Note: this is a tree-level helper function for an ensemble-level function. It assumes the creation of:

a vector of column indices of size dataset.NumObservations() x ensemble.NumTrees(), stored in "tree-major" order

a running counter of the number of tree-observations already indexed in the ensemble
(used as offsets for the leaf number computed and returned here) Users running this function for a single tree may simply pre-allocate an output vector as std::vector<int32_t> output(dataset->NumObservations()) and set the offset to 0.

Parameters

covariates	Eigen matrix with which to predict leaf indices
output	Pre-allocated output vector storing a matrix of column indices, with "rows" corresponding to observations in `covariates` and "columns" corresponding to trees in an ensemble
offset	Bookkeeping index that determines where in `output` vector that column indices should be unpacked
max_leaf	Largest leaf value mapped so far. (Leaf indices serve as sparse column indices, so it is important that leaf values be unique to each tree.)

◆ PredictLeafIndexInplace() [3/3]

void StochTree::Tree::PredictLeafIndexInplace	(	Eigen::Map< Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor > > &	covariates,
		std::vector< int32_t > &	output,
		int32_t	offset,
		int32_t	max_leaf
	)

Obtain a 0-based leaf index for each observation in a ForestDataset. Internally, trees are stored as vectors of node information, and the leaves_ vector gives us node IDs for every leaf in the tree. Here, we would like to know, for every observation in a dataset, which leaf number it is mapped to. Since the leaf numbers themselves do not carry any information, we renumber them from 0 to leaves_.size()-1.

Note: this is a tree-level helper function for an ensemble-level function. It assumes the creation of:

a vector of column indices of size dataset.NumObservations() x ensemble.NumTrees(), stored in "tree-major" order

a running counter of the number of tree-observations already indexed in the ensemble
(used as offsets for the leaf number computed and returned here) Users running this function for a single tree may simply pre-allocate an output vector as std::vector<int32_t> output(dataset->NumObservations()) and set the offset to 0.

Parameters

covariates	Eigen matrix with which to predict leaf indices
output	Pre-allocated output vector storing a matrix of column indices, with "rows" corresponding to observations in `covariates` and "columns" corresponding to trees in an ensemble
offset	Bookkeeping index that determines where in `output` vector that column indices should be unpacked
max_leaf	Largest leaf value mapped so far. (Leaf indices serve as sparse column indices, so it is important that leaf values be unique to each tree.)

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

include/stochtree/tree.h

Public Member Functions

Detailed Description

Member Function Documentation

◆ CloneFromTree()

◆ from_json()

◆ CollapseToLeaf() [1/2]

◆ CollapseToLeaf() [2/2]

◆ WalkTree()

◆ HasVectorOutput()

◆ Parent()

◆ LeftChild()

◆ RightChild()

◆ DefaultChild()

◆ SplitIndex()

◆ IsLeaf()

◆ IsRoot()

◆ IsDeleted()

◆ LeafValue() [1/2]

◆ LeafValue() [2/2]

◆ LeafVector()

◆ SumSquaredNodeValues()

◆ HasLeafVector()

◆ Threshold()

◆ CategoryList()

◆ NodeType()

◆ IsNumericSplitNode()

◆ IsCategoricalSplitNode()

◆ GetDepth()

◆ SetLeftChild()

◆ SetRightChild()

◆ SetChildren()

◆ SetParent()

◆ SetParents()

◆ SetNumericSplit()

◆ SetCategoricalSplit()

◆ SetLeaf()

◆ SetLeafVector()

◆ PredictLeafIndexInplace() [1/3]

◆ PredictLeafIndexInplace() [2/3]

◆ PredictLeafIndexInplace() [3/3]