A projection pursuit decision tree for classification. More...

#include <ClassificationTree.hpp>

Public Types
using	FeatureMatrix = types::FeatureMatrix

using	FeatureVector = types::FeatureVector

using	GroupIndices = std::map<types::GroupId, int>

using	GroupPartition = stats::GroupPartition

using	Groups = std::set<types::GroupId>

using	Outcome = types::Outcome

using	OutcomeVector = types::OutcomeVector

using	Ptr = std::unique_ptr<ClassificationTree>

using	RNG = stats::RNG

Public Types inherited from ppforest2::Tree
using	FeatureMatrix = types::FeatureMatrix

using	FeatureVector = types::FeatureVector

using	GroupPartition = stats::GroupPartition

using	Outcome = types::Outcome

using	OutcomeVector = types::OutcomeVector

using	Ptr = std::unique_ptr<Tree>

using	RNG = stats::RNG

using	Root = TreeNode::Ptr

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

Public Member Functions
	ClassificationTree (TreeNode::Ptr root, TrainingSpec::Ptr spec, Groups groups)

void	accept (Model::Visitor &visitor) const override
	Accept a model visitor (mode-specific dispatch).

FeatureMatrix	predict (FeatureMatrix const &x, Proportions) const
	One-hot encoding of the predicted group per row, columns laid out by `groups()`.

FeatureMatrix	predict (FeatureMatrix const &x, Proportions, GroupIndices const &indices) const
	One-hot encoding per row, with an explicit column layout.

FeatureVector	predict (FeatureVector const &x, Proportions, GroupIndices const &indices) const
	One-hot encoding for one observation, with an explicit column layout passed as a precomputed `{group → column}` map.

types::Outcome	predict (types::FeatureVector const &x) const override
	Predict a single observation.

types::FeatureVector	predict (types::FeatureVector const &x, Proportions) const
	One-hot encoding of the predicted group for one observation, with columns laid out by `groups()`.

Public Member Functions inherited from ppforest2::Tree
bool	operator!= (Tree const &other) const

bool	operator== (Tree const &other) const

virtual types::OutcomeVector	predict (types::FeatureMatrix const &x) const
	Predict a matrix of observations.

types::Outcome	predict (types::FeatureVector const &x) const override
	Predict a single observation.

Public Member Functions inherited from ppforest2::Model
virtual	~Model ()=default

Static Public Member Functions
static Ptr	train (TrainingSpec const &s, FeatureMatrix &x, OutcomeVector &y, GroupPartition const &y_part, RNG &rng)
	Train a classification tree with an external RNG.

Static Public Member Functions inherited from ppforest2::Tree
static Ptr	train (TrainingSpec const &spec, types::FeatureMatrix &x, types::OutcomeVector &y)
	Train a tree from a response vector.

static Ptr	train (TrainingSpec const &spec, types::FeatureMatrix &x, types::OutcomeVector &y, stats::RNG &rng)
	Train a tree from a response vector.

Static Public Member Functions inherited from ppforest2::Model
static void	check_train_inputs (types::FeatureMatrix const &x, types::OutcomeVector const &y)
	Validate common training inputs (y non-empty, matching x rows).

static Ptr	train (TrainingSpec const &spec, types::FeatureMatrix &x, types::OutcomeVector &y)
	Train a model from a training specification.

Public Attributes
Groups	groups
	Set of group labels this tree predicts over.

Public Attributes inherited from ppforest2::Tree
Root	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
Protected Member Functions inherited from ppforest2::Tree
	Tree (TreeNode::Ptr root, TrainingSpec::Ptr spec)

Static Protected Member Functions inherited from ppforest2::Tree
static Root	build_root (TrainingSpec const &spec, FeatureMatrix &x, OutcomeVector &y, GroupPartition const &y_part, RNG &rng)
	Build the root node of a tree.

Detailed Description

A projection pursuit decision tree for classification.

Leaves hold integer group labels produced by the configured leaf strategy (default MajorityVote). The predict(data, Proportions) overload returns a one-hot encoding of the predicted group.

Member Typedef Documentation

◆ FeatureMatrix

using ppforest2::ClassificationTree::FeatureMatrix = types::FeatureMatrix

◆ FeatureVector

using ppforest2::ClassificationTree::FeatureVector = types::FeatureVector

◆ GroupIndices

using ppforest2::ClassificationTree::GroupIndices = std::map<types::GroupId, int>

◆ GroupPartition

using ppforest2::ClassificationTree::GroupPartition = stats::GroupPartition

◆ Groups

using ppforest2::ClassificationTree::Groups = std::set<types::GroupId>

◆ Outcome

using ppforest2::ClassificationTree::Outcome = types::Outcome

◆ OutcomeVector

using ppforest2::ClassificationTree::OutcomeVector = types::OutcomeVector

◆ Ptr

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

◆ RNG

using ppforest2::ClassificationTree::RNG = stats::RNG

Constructor & Destructor Documentation

◆ ClassificationTree()

ppforest2::ClassificationTree::ClassificationTree	(	TreeNode::Ptr	root,
		TrainingSpec::Ptr	spec,
		Groups	groups )

inline

Member Function Documentation

◆ accept()

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

overridevirtual

Accept a model visitor (mode-specific dispatch).

Implements ppforest2::Tree.

◆ predict() [1/5]

FeatureMatrix ppforest2::ClassificationTree::predict	(	FeatureMatrix const &	x,
		Proportions	) const

One-hot encoding of the predicted group per row, columns laid out by groups().

◆ predict() [2/5]

FeatureMatrix ppforest2::ClassificationTree::predict	(	FeatureMatrix const &	x,
		Proportions	,
		GroupIndices const &	indices ) const

One-hot encoding per row, with an explicit column layout.

Iterates rows calling the single-row predict(..., indices) overload. ClassificationForest::predict(..., Proportions) reuses this so every bagged tree's proportions land in the forest's column layout.

◆ predict() [3/5]

FeatureVector ppforest2::ClassificationTree::predict	(	FeatureVector const &	x,
		Proportions	,
		GroupIndices const &	indices ) const

One-hot encoding for one observation, with an explicit column layout passed as a precomputed {group → column} map.

Base primitive. indices must contain every label this tree can predict (otherwise at() throws). The matrix overload, and ClassificationForest, both build the indices once at entry and call this overload per row so the map is never rebuilt in a loop.

◆ predict() [4/5]

types::Outcome ppforest2::Tree::predict ( types::FeatureVector const & x ) const

overridevirtual

Predict a single observation.

Walks the tree and returns the leaf value. Same implementation for both modes — the leaf value is produced by the mode-specific leaf strategy during training.

Implements ppforest2::Model.

◆ predict() [5/5]

types::FeatureVector ppforest2::ClassificationTree::predict	(	types::FeatureVector const &	x,
		Proportions	) const

One-hot encoding of the predicted group for one observation, with columns laid out by groups().

◆ train()

static Ptr ppforest2::ClassificationTree::train	(	TrainingSpec const &	s,
		FeatureMatrix &	x,
		OutcomeVector &	y,
		GroupPartition const &	y_part,
		RNG &	rng )

static

Train a classification tree with an external RNG.

x and y are non-const for signature symmetry with RegressionTree::train — classification doesn't mutate either.

Parameters

s	Training specification (must have `mode = Classification`).
x	Feature matrix (n × p).
y	Response vector (integer class labels encoded as floats).
y_part	Initial root group partition.
rng	Random number generator.

Member Data Documentation

◆ groups

Groups ppforest2::ClassificationTree::groups

Set of group labels this tree predicts over.

Canonical column layout for predict(FeatureMatrix, Proportions). Populated by train (from the parent's y_part.groups) and by the JSON deserializer (from meta.groups). Independent of root->node_groups() so that a stop/leaf combination producing a single-leaf root doesn't shrink the prediction matrix relative to what the model was trained for.

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

core/src/models/ClassificationTree.hpp

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Additional Inherited Members