This class implements a generic decision tree learner. More...

Inheritance diagram for mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >:

[legend]

Public Types
typedef CategoricalSplitType< FitnessFunction >	CategoricalSplit
	Allow access to the categorical split type. More...

typedef NumericSplitType< FitnessFunction >	NumericSplit
	Allow access to the numeric split type. More...

Public Member Functions
template<typename MatType >
	DecisionTree (const MatType &data, const data::DatasetInfo &datasetInfo, const arma::Row< size_t > &labels, const size_t numClasses, const size_t minimumLeafSize=10)
	Construct the decision tree on the given data and labels, where the data can be both numeric and categorical. More...

template<typename MatType >
	DecisionTree (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, const size_t minimumLeafSize=10)
	Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type. More...

	DecisionTree (const size_t numClasses=1)
	Construct a decision tree without training it. More...

	DecisionTree (const DecisionTree &other)
	Copy another tree. More...

	DecisionTree (DecisionTree &&other)
	Take ownership of another tree. More...

	~DecisionTree ()
	Clean up memory. More...

template<typename VecType >
size_t	CalculateDirection (const VecType &point) const
	Given a point and that this node is not a leaf, calculate the index of the child node this point would go towards. More...

const DecisionTree &	Child (const size_t i) const
	Get the child of the given index. More...

DecisionTree &	Child (const size_t i)
	Modify the child of the given index (be careful!). More...

template<typename VecType >
size_t	Classify (const VecType &point) const
	Classify the given point, using the entire tree. More...

template<typename VecType >
void	Classify (const VecType &point, size_t &prediction, arma::vec &probabilities) const
	Classify the given point and also return estimates of the probability for each class in the given vector. More...

template<typename MatType >
void	Classify (const MatType &data, arma::Row< size_t > &predictions) const
	Classify the given points, using the entire tree. More...

template<typename MatType >
void	Classify (const MatType &data, arma::Row< size_t > &predictions, arma::mat &probabilities) const
	Classify the given points and also return estimates of the probabilities for each class in the given matrix. More...

size_t	NumChildren () const
	Get the number of children. More...

DecisionTree &	operator= (const DecisionTree &other)
	Copy another tree. More...

DecisionTree &	operator= (DecisionTree &&other)
	Take ownership of another tree. More...

template<typename Archive >
void	Serialize (Archive &ar, const unsigned int)
	Serialize the tree. More...

template<typename MatType >
void	Train (const MatType &data, const data::DatasetInfo &datasetInfo, const arma::Row< size_t > &labels, const size_t numClasses, const size_t minimumLeafSize=10)
	Train the decision tree on the given data. More...

template<typename MatType >
void	Train (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, const size_t minimumLeafSize=10)
	Train the decision tree on the given data, assuming that all dimensions are numeric. More...

Private Types
typedef CategoricalSplit::template AuxiliarySplitInfo< ElemType >	CategoricalAuxiliarySplitInfo

typedef NumericSplit::template AuxiliarySplitInfo< ElemType >	NumericAuxiliarySplitInfo
	Note that this class will also hold the members of the NumericSplit and CategoricalSplit AuxiliarySplitInfo classes, since it inherits from them. More...

Private Member Functions
template<typename RowType >
void	CalculateClassProbabilities (const RowType &labels, const size_t numClasses)
	Calculate the class probabilities of the given labels. More...

Private Attributes
std::vector< DecisionTree * >	children
	The vector of children. More...

arma::vec	classProbabilities
	This vector may hold different things. More...

size_t	dimensionTypeOrMajorityClass
	The type of the dimension that we have split on (if we are not a leaf). More...

size_t	splitDimension
	The dimension this node splits on. More...

Detailed Description

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>
class mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >

This class implements a generic decision tree learner.

Its behavior can be controlled via its template arguments.

The class inherits from the auxiliary split information in order to prevent an empty auxiliary split information struct from taking any extra size.

Definition at line 31 of file decision_tree.hpp.

Member Typedef Documentation

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

typedef CategoricalSplit::template AuxiliarySplitInfo<ElemType> mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::CategoricalAuxiliarySplitInfo

private

Definition at line 254 of file decision_tree.hpp.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

typedef CategoricalSplitType<FitnessFunction> mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::CategoricalSplit

Allow access to the categorical split type.

Definition at line 41 of file decision_tree.hpp.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

typedef NumericSplit::template AuxiliarySplitInfo<ElemType> mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::NumericAuxiliarySplitInfo

private

Note that this class will also hold the members of the NumericSplit and CategoricalSplit AuxiliarySplitInfo classes, since it inherits from them.

We'll define some convenience typedefs here.

Definition at line 252 of file decision_tree.hpp.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

typedef NumericSplitType<FitnessFunction> mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::NumericSplit

Allow access to the numeric split type.

Definition at line 39 of file decision_tree.hpp.

Constructor & Destructor Documentation

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename MatType >

mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::DecisionTree	(	const MatType &	data,
		const data::DatasetInfo &	datasetInfo,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`
	)

Construct the decision tree on the given data and labels, where the data can be both numeric and categorical.

Setting minimumLeafSize too small may cause the tree to overfit, but setting it too large may cause it to underfit.

Parameters

data	Dataset to train on.
datasetInfo	Type information for each dimension of the dataset.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename MatType >

mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::DecisionTree	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`
	)

Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type.

Setting minimumLeafSize too small may cause the tree to overfit, but setting it too large may cause it to underfit.

Parameters

data	Dataset to train on.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::DecisionTree ( const size_t numClasses = 1 )

Construct a decision tree without training it.

It will be a leaf node with equal probabilities for each class.

Parameters

numClasses Number of classes in the dataset.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::DecisionTree ( const DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion > & other )

Copy another tree.

This may use a lot of memory—be sure that it's what you want to do.

Parameters

other Tree to copy.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::DecisionTree ( DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion > && other )

Take ownership of another tree.

Parameters

other Tree to take ownership of.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::~DecisionTree ( )

Clean up memory.

Member Function Documentation

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename RowType >

void mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::CalculateClassProbabilities	(	const RowType &	labels,
		const size_t	numClasses
	)

private

Calculate the class probabilities of the given labels.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename VecType >

size_t mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::CalculateDirection ( const VecType & point ) const

Given a point and that this node is not a leaf, calculate the index of the child node this point would go towards.

This method is primarily used by the Classify() function, but it can be used in a standalone sense too.

Parameters

point Point to classify.

Referenced by mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Child().

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

const DecisionTree& mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Child ( const size_t i ) const

inline

Get the child of the given index.

Definition at line 217 of file decision_tree.hpp.

References mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::children.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

DecisionTree& mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Child ( const size_t i )

inline

Modify the child of the given index (be careful!).

Definition at line 219 of file decision_tree.hpp.

References mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::CalculateDirection(), and mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::children.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename VecType >

size_t mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Classify ( const VecType & point ) const

Classify the given point, using the entire tree.

The predicted label is returned.

Parameters

point Point to classify.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename VecType >

void mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Classify	(	const VecType &	point,
		size_t &	prediction,
		arma::vec &	probabilities
	)		const

Classify the given point and also return estimates of the probability for each class in the given vector.

Parameters

point	Point to classify.
prediction	This will be set to the predicted class of the point.
probabilities	This will be filled with class probabilities for the point.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename MatType >

void mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Classify	(	const MatType &	data,
		arma::Row< size_t > &	predictions
	)		const

Classify the given points, using the entire tree.

The predicted labels for each point are stored in the given vector.

Parameters

data	Set of points to classify.
predictions	This will be filled with predictions for each point.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename MatType >

void mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Classify	(	const MatType &	data,
		arma::Row< size_t > &	predictions,
		arma::mat &	probabilities
	)		const

Classify the given points and also return estimates of the probabilities for each class in the given matrix.

The predicted labels for each point are stored in the given vector.

Parameters

data	Set of points to classify.
predictions	This will be filled with predictions for each point.
probabilities	This will be filled with class probabilities for each point.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

size_t mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::NumChildren ( ) const

inline

Get the number of children.

Definition at line 214 of file decision_tree.hpp.

References mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::children.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

DecisionTree& mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::operator= ( const DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion > & other )

Copy another tree.

This may use a lot of memory—be sure that it's what you want to do.

Parameters

other Tree to copy.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

DecisionTree& mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::operator= ( DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion > && other )

Take ownership of another tree.

Parameters

other Tree to take ownership of.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename Archive >

void mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the tree.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename MatType >

void mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Train	(	const MatType &	data,
		const data::DatasetInfo &	datasetInfo,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`
	)

Train the decision tree on the given data.

This will overwrite the existing model. The data may have numeric and categorical types, specified by the datasetInfo parameter. Setting minimumLeafSize too small may cause the tree to overfit, but setting it too large may cause it to underfit.

Parameters

data	Dataset to train on.
datasetInfo	Type information for each dimension.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

template<typename MatType >

void mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`
	)

Train the decision tree on the given data, assuming that all dimensions are numeric.

This will overwrite the given model. Setting minimumLeafSize too small may cause the tree to overfit, but setting it too large may cause it to underfit.

Parameters

data	Dataset to train on.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.

Member Data Documentation

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

std::vector<DecisionTree*> mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::children

private

The vector of children.

Definition at line 233 of file decision_tree.hpp.

Referenced by mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::Child(), and mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::NumChildren().

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

arma::vec mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::classProbabilities

private

This vector may hold different things.

If the node has no children, then it is guaranteed to hold the probabilities of each class. If the node has children, then it may be used arbitrarily by the split type's CalculateDirection() function and may not necessarily hold class probabilities.

Definition at line 246 of file decision_tree.hpp.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

size_t mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::dimensionTypeOrMajorityClass

private

The type of the dimension that we have split on (if we are not a leaf).

If we are a leaf, then this is the index of the majority class.

Definition at line 238 of file decision_tree.hpp.

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename ElemType = double, bool NoRecursion = false>

size_t mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion >::splitDimension

private

The dimension this node splits on.

Definition at line 235 of file decision_tree.hpp.

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

src/mlpack/methods/decision_tree/decision_tree.hpp

Public Types

Public Member Functions

Private Types

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation