ppforest2::BootstrapTree Struct Reference

#include <BootstrapTree.hpp>

Public Types
using	Ptr = std::unique_ptr<BootstrapTree>
Public Types inherited from ppforest2::Model
using	Ptr = std::shared_ptr<Model>

Public Member Functions
	BootstrapTree (TreeNode::Ptr root, TrainingSpec::Ptr spec, std::vector< int > samp)
std::vector< int >	oob_indices (int n_total) const
	Indices of observations not in the bootstrap sample.
types::ResponseVector	predict_oob (types::FeatureMatrix const &x, std::vector< int > const &row_idx) const
	Predict for a subset of rows (e.g. OOB indices).
	Tree (TreeNode::Ptr root, TrainingSpec::Ptr training_spec)
Public Member Functions inherited from ppforest2::Tree
	Tree (TreeNode::Ptr root, TrainingSpec::Ptr training_spec)
void	accept (Model::Visitor &visitor) const override
	Accept a model visitor (double dispatch).
bool	operator!= (Tree const &other) const
bool	operator== (Tree const &other) const
types::ResponseVector	predict (types::FeatureMatrix const &data) const override
	Predict a matrix of observations.
types::FeatureMatrix	predict (types::FeatureMatrix const &data, Proportions) const override
	Predict proportions for a matrix of observations.
types::Response	predict (types::FeatureVector const &data) const override
	Predict a single observation.
Public Member Functions inherited from ppforest2::Model
virtual	~Model ()=default

Static Public Member Functions
static Ptr	train (TrainingSpec::Ptr const &training_spec, types::FeatureMatrix const &x, stats::GroupPartition const &group_spec, stats::RNG &rng)
	Train a bootstrap tree.
Static Public Member Functions inherited from ppforest2::Tree
static Tree	train (TrainingSpec const &training_spec, types::FeatureMatrix const &x, stats::GroupPartition const &group_spec, stats::RNG &rng)
	Train a tree from a group partition.
static Tree	train (TrainingSpec const &training_spec, types::FeatureMatrix const &x, types::ResponseVector const &y, stats::RNG &rng)
	Train a tree from a response vector.
Static Public Member Functions inherited from ppforest2::Model
static Ptr	train (TrainingSpec const &spec, types::FeatureMatrix const &x, types::ResponseVector const &y)
	Train a model from a training specification.

Public Attributes
std::vector< int >	sample_indices
Public Attributes inherited from ppforest2::Tree
TreeNode::Ptr	root
	Root node of the tree.
Public Attributes inherited from ppforest2::Model
bool	degenerate = false
	Whether the model contains degenerate nodes/splits.
TrainingSpec::Ptr	training_spec
	Training specification used to build this model.

Member Typedef Documentation

Ptr

using ppforest2::BootstrapTree::Ptr = std::unique_ptr<BootstrapTree>

Constructor & Destructor Documentation

BootstrapTree()

ppforest2::BootstrapTree::BootstrapTree	(	TreeNode::Ptr	root,
		TrainingSpec::Ptr	spec,
		std::vector< int >	samp )

Member Function Documentation

oob_indices()

std::vector< int > ppforest2::BootstrapTree::oob_indices

(

int

n_total

)

const

Indices of observations not in the bootstrap sample.

Parameters

n_total

Total number of observations in the training set.

Returns

Sorted vector of out-of-bag row indices.

predict_oob()

types::ResponseVector ppforest2::BootstrapTree::predict_oob	(	types::FeatureMatrix const &	x,
		std::vector< int > const &	row_idx ) const

Predict for a subset of rows (e.g. OOB indices).

Typically used with oob_indices() to obtain predictions for out-of-bag observations only. The returned vector has the same size as row_idx; element i is the prediction for row row_idx[i].

Parameters

x	Feature matrix (n × p).
row_idx	Row indices to predict.

Returns

Predictions for each row in row_idx.

train()

static Ptr ppforest2::BootstrapTree::train ( TrainingSpec::Ptr const & training_spec, types::FeatureMatrix const & x, stats::GroupPartition const & group_spec, stats::RNG & rng )

static

Train a bootstrap tree.

Samples rows from x according to group_spec, trains a tree on the sampled data, and stores the sample indices for OOB queries.

Parameters

training_spec	Training specification (shared).
x	Feature matrix (n × p).
group_spec	Group partition for stratified sampling.
rng	Random number generator.

Returns

Trained bootstrap tree.

Tree()

ppforest2::Tree::Tree	(	TreeNode::Ptr	root,
		TrainingSpec::Ptr	training_spec )

Member Data Documentation

sample_indices

std::vector<int> ppforest2::BootstrapTree::sample_indices

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

• core/src/models/BootstrapTree.hpp