ppforest2::Tree Struct Reference

A single projection pursuit decision tree. More...

#include <Tree.hpp>

Public Member Functions
	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 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
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.

Additional Inherited Members
Public Types inherited from ppforest2::Model
using	Ptr = std::shared_ptr<Model>

Detailed Description

A single projection pursuit decision tree.

Each internal node projects data onto a linear combination of features and splits on the projected value. Leaf nodes hold group labels.

TrainingSpec spec(pp::pda(0.0), dr::noop(), sr::mean_of_means());
stats::RNG rng(0);
 
Tree tree = Tree::train(spec, x, y, rng);
types::Response label = tree.predict(x.row(0));
types::ResponseVector preds = tree.predict(x);

Constructor & Destructor Documentation

Tree()

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

Member Function Documentation

accept()

void ppforest2::Tree::accept ( Model::Visitor & visitor ) const

overridevirtual

Accept a model visitor (double dispatch).

Implements ppforest2::Model.

operator!=()

bool ppforest2::Tree::operator!=

(

Tree const &

other

)

const

operator==()

bool ppforest2::Tree::operator==

(

Tree const &

other

)

const

predict() [1/3]

types::ResponseVector ppforest2::Tree::predict ( types::FeatureMatrix const & data ) const

overridevirtual

Predict a matrix of observations.

Parameters

data	Feature matrix (n × p).

Returns

Predicted group labels (n).

Implements ppforest2::Model.

predict() [2/3]

types::FeatureMatrix ppforest2::Tree::predict ( types::FeatureMatrix const & data, Proportions  ) const

overridevirtual

Predict proportions for a matrix of observations.

Returns an (n × G) matrix. For forests, each row contains the fraction of trees that voted for each group. For single trees, each row is a one-hot encoding of the predicted group.

Parameters

data	Feature matrix (n × p).

Returns

Proportion matrix (n × G), rows sum to 1.0.

Implements ppforest2::Model.

predict() [3/3]

types::Response ppforest2::Tree::predict ( types::FeatureVector const & data ) const

overridevirtual

Predict a single observation.

Parameters

data	Feature vector (p).

Returns

Predicted group label.

Implements ppforest2::Model.

train() [1/2]

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

static

Train a tree from a group partition.

Recursively splits the data by finding optimal projections at each node until pure leaves are reached.

Parameters

training_spec	Training specification (strategy + DR).
x	Feature matrix (n × p).
group_spec	Group partition.
rng	Random number generator.

Returns

Trained tree.

train() [2/2]

static Tree ppforest2::Tree::train ( TrainingSpec const & training_spec, types::FeatureMatrix const & x, types::ResponseVector const & y, stats::RNG & rng )

static

Train a tree from a response vector.

Constructs a GroupPartition from y and delegates to the GroupPartition overload.

Parameters

training_spec	Training specification (strategy + DR).
x	Feature matrix (n × p).
y	Response vector (n).
rng	Random number generator.

Returns

Trained tree.

Member Data Documentation

root

TreeNode::Ptr ppforest2::Tree::root

Root node of the tree.

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

• core/src/models/Tree.hpp