WeightedEnsemble Class Reference

Combining several predictors with a weighted ensemble. More...

Inheritance diagram for WeightedEnsemble:

List of all members.

Public Member Functions
virtual bool	CanPredict (int user_id, int item_id)
	Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination.
Object	Clone ()
	create a shallow copy of the object
override void	LoadModel (string file)
	Get the model parameters from a file.
override float	Predict (int user_id, int item_id)
	Predict rating or score for a given user-item combination.
override void	SaveModel (string file)
	Save the model parameters to a file.
string	ToString ()
	Return a string representation of the recommender.
override void	Train ()
	Learn the model parameters of the recommender from the training data.
Public Attributes
List< IRecommender >	recommenders = new List<IRecommender>()
	list of recommenders
List< float >	weights = new List<float>()
	List of component weights.
Protected Attributes
double	weight_sum
	Sum of the component weights.
Properties
float	MaxRating [get, set]
	The max rating value.
float	MinRating [get, set]
	The min rating value.

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Detailed Description

Combining several predictors with a weighted ensemble.

This recommender does NOT support incremental updates.

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Member Function Documentation

virtual bool CanPredict	(	int	user_id,
		int	item_id
	)		[inline, virtual, inherited]

Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination.

It is up to the recommender implementor to decide when a prediction is useful, and to document it accordingly.

Parameters:

user_id	the user ID
item_id	the item ID

Returns:

true if a useful prediction can be made, false otherwise

Implements IRecommender.

Object Clone

(

)

[inline, inherited]

create a shallow copy of the object

override void LoadModel

(

string

filename

)

[inline, virtual]

Get the model parameters from a file.

Parameters:

filename

the name of the file to read from

Implements Ensemble.

override float Predict	(	int	user_id,
		int	item_id
	)		[inline, virtual]

Predict rating or score for a given user-item combination.

Parameters:

user_id	the user ID
item_id	the item ID

Returns:

the predicted score/rating for the given user-item combination

Implements Ensemble.

override void SaveModel

(

string

filename

)

[inline, virtual]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Implements Ensemble.

string ToString

(

)

[inherited]

Return a string representation of the recommender.

The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.

Implemented in BPRMF, BiasedMatrixFactorization, BPRMF_Mapping, SVDPlusPlus, MatrixFactorization, CoClustering, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, SigmoidSVDPlusPlus, BPRLinear, UserItemBaseline, BPRMF_ItemMapping, SocialMF, BPRMF_UserMapping, NaiveBayes, TimeAwareBaselineWithFrequencies, WRMF, MultiCoreBPRMF, BPRMF_ItemMapping_Optimal, BPRMF_ItemMappingSVR, SoftMarginRankingMF, ItemAttributeSVM, BPRMF_UserMapping_Optimal, BPRMF_ItemMappingKNN, ItemRecommender, RatingPredictor, ItemKNN, UserKNN, UserAttributeKNN, WeightedBPRMF, UserKNNCosine, Constant, UserKNNPearson, ItemAttributeKNN, ItemAttributeKNN, UserAttributeKNN, WeightedItemKNN, ItemKNNPearson, WeightedUserKNN, and ItemKNNCosine.

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Member Data Documentation

List<IRecommender> recommenders = new List<IRecommender>() [inherited]

list of recommenders

double weight_sum [protected]

Sum of the component weights.

List<float> weights = new List<float>()

List of component weights.

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Property Documentation

float MaxRating [get, set, inherited]

The max rating value.

The max rating value

float MinRating [get, set, inherited]

The min rating value.

The min rating value

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

• WeightedEnsemble.cs