#include <submodel.h>

Inheritance diagram for dismec::model::SubModelWrapper< T >:

Public Member Functions
	SubModelWrapper (T original, label_id_t begin, label_id_t end)

long	num_features () const override
	How many weights are in each weight vector, i.e. how many features should the input have. More...

bool	has_sparse_weights () const override
	whether this model stores the weights in a sparse format, or a dense format. More...

void	get_weights_for_label_unchecked (label_id_t label, Eigen::Ref< DenseRealVector > target) const override
	Unchecked version of get_weights_for_label(). More...

void	set_weights_for_label_unchecked (label_id_t label, const GenericInVector &weights) override
	Unchecked version of set_weights_for_label(). More...

void	predict_scores_unchecked (const GenericInMatrix &instances, PredictionMatrixOut target) const override
	Unchecked version of predict_scores(). More...

Public Member Functions inherited from dismec::model::Model
	Model (PartialModelSpec spec)

virtual	~Model ()=default

long	num_labels () const noexcept
	How many labels are in the underlying dataset. More...

long	num_weights () const noexcept
	How many weights vectors are in this model. More...

bool	is_partial_model () const
	returns true if this instance only stores part of the weights of an entire model More...

label_id_t	labels_begin () const noexcept

label_id_t	labels_end () const noexcept

long	contained_labels () const noexcept
	How many labels are in this submodel. More...

void	get_weights_for_label (label_id_t label, Eigen::Ref< DenseRealVector > target) const
	Gets the weights for the given label as a dense vector. More...

void	set_weights_for_label (label_id_t label, const WeightVectorIn &weights)
	Sets the weights for a label. More...

void	predict_scores (const FeatureMatrixIn &instances, PredictionMatrixOut target) const
	Calculates the scores for all examples and all labels in this model. More...

Private Types
using	model_t = std::remove_reference_t< decltype(*std::declval< T >())>

Private Attributes
T	m_Original

Additional Inherited Members
Public Types inherited from dismec::model::Model
using	PredictionMatrixOut = Eigen::Ref< PredictionMatrix >

using	FeatureMatrixIn = GenericInMatrix

using	WeightVectorIn = GenericInVector

Protected Member Functions inherited from dismec::model::Model
label_id_t	adjust_label (label_id_t label) const

Detailed Description

template<class T>
class dismec::model::SubModelWrapper< T >

Definition at line 13 of file submodel.h.

Member Typedef Documentation

◆ model_t

template<class T >

using dismec::model::SubModelWrapper< T >::model_t = std::remove_reference_t<decltype(*std::declval<T>())>

private

Definition at line 14 of file submodel.h.

Constructor & Destructor Documentation

◆ SubModelWrapper()

template<class T >

dismec::model::SubModelWrapper< T >::SubModelWrapper	(	T	original,
		label_id_t	begin,
		label_id_t	end
	)

inline

Definition at line 17 of file submodel.h.

Member Function Documentation

◆ get_weights_for_label_unchecked()

template<class T >

void dismec::model::SubModelWrapper< T >::get_weights_for_label_unchecked	(	label_id_t	label,
		Eigen::Ref< DenseRealVector >	target
	)		const

inlineoverridevirtual

Unchecked version of get_weights_for_label().

Since we do not know whether the weights saved in the model are sparse or dense vectors, we cannot simply return a const reference here. Instead, the user is required to provide a pre-allocated buffer target into which the weights will be copied.

Exceptions

If	`target` does not have the correct size, or if `label` is invalid.

Note: This function is called from get_weights_for_label and can assume label is a valid label index that has been corrected for partial models (i.e. such that the first label of the partial model will get the index 0). Target can be assumed to be of correct size.

Implements dismec::model::Model.

Definition at line 25 of file submodel.h.

References dismec::model::Model::labels_begin(), dismec::model::SubModelWrapper< T >::m_Original, and dismec::opaque_int_type< Tag, T >::to_index().

◆ has_sparse_weights()

template<class T >

bool dismec::model::SubModelWrapper< T >::has_sparse_weights ( ) const

inlineoverridevirtual

whether this model stores the weights in a sparse format, or a dense format.

Implements dismec::model::Model.

Definition at line 23 of file submodel.h.

References dismec::model::SubModelWrapper< T >::m_Original.

◆ num_features()

template<class T >

long dismec::model::SubModelWrapper< T >::num_features ( ) const

inlineoverridevirtual

How many weights are in each weight vector, i.e. how many features should the input have.

Implements dismec::model::Model.

Definition at line 22 of file submodel.h.

References dismec::model::SubModelWrapper< T >::m_Original.

◆ predict_scores_unchecked()

template<class T >

void dismec::model::SubModelWrapper< T >::predict_scores_unchecked	(	const GenericInMatrix &	instances,
		PredictionMatrixOut	target
	)		const

inlineoverridevirtual

Unchecked version of predict_scores().

This is just the matrix multiplication of the input instances and the weight matrix. This function can be called safely from multiple threads. Note that Eigen::Ref requires that the passed submatrix has an inner stride of 1, i.e. that features for a single instance are provided as contiguous memory (in case of dense features), and the same for the pre-allocated buffer for the targets. This can be achieved e.g. by using rows of a row-major matrix.

Parameters

instances	Feature vector of the instances for which we want to predict the scores. This is handled as a `Eigen::Ref` parameter so that subsets of a large dataset can be passed without needing data to be copied. Should have number of columns equal to the number of features. The `GenericInMatrix` allows different data formats to be passed – however, some data formats may be more efficient than others.
target	This is the matrix to which the scores will be written. Has to have the correct size, i.e. the same number of rows as `instances` and number of columns equal to the number of labels.

Exceptions

If	`instances` and `target` have different number of rows, or if the number of columns (rows) in `instances` (`target`) does not match `get_num_features()` (`get_num_labels()`).

Note: This function is called from predict_scores and can assume that the shapes of instances and target have been verified.

Implements dismec::model::Model.

Definition at line 38 of file submodel.h.

◆ set_weights_for_label_unchecked()

template<class T >

void dismec::model::SubModelWrapper< T >::set_weights_for_label_unchecked	(	label_id_t	label,
		const GenericInVector &	weights
	)

inlineoverridevirtual

Unchecked version of set_weights_for_label().

This assigns the given vector as weights for the labelth label. For non-overlapping label parameters this function can safely be called concurrently from different threads. This function can be used for both dense and sparse internal weight representation. If the model internally uses a sparse representation, the zeros will be filtered out.

Exceptions

If	`weights` does not have the correct size, or if `label` is invalid.

Note: This function is called from set_weights_for_label and can assume label is a valid label index that has been corrected for partial models (i.e. such that the first label of the partial model will get the index 0). weights can be assumed to be of correct size.

Implements dismec::model::Model.

Definition at line 29 of file submodel.h.

References dismec::model::Model::labels_begin(), dismec::model::SubModelWrapper< T >::m_Original, and dismec::opaque_int_type< Tag, T >::to_index().