GClasses
GClasses::GContentBoostedCF Class Reference

#include <GRecommender.h>

Inheritance diagram for GClasses::GContentBoostedCF:
GClasses::GCollaborativeFilter

Public Member Functions

 GContentBoostedCF (GArgReader copy)
 
virtual ~GContentBoostedCF ()
 Destructor. More...
 
virtual void impute (GVec &vec, size_t dims)
 pVec should be a vector of n real values, where n is the number of items/attributes/columns in the data that was used to train the model. to UNKNOWN_REAL_VALUE. This method will evaluate the known elements and impute (predict) values for the unknown elements. (The model should be trained before this method is called. Unlike the predict method, this method can operate on row-vectors that were not part of the training data.) More...
 
virtual double predict (size_t user, size_t item)
 This returns a prediction for how the specified user will rate the specified item. (The model must be trained before this method is called. Also, some values for that user and item should have been included in the training set, or else this method will have no basis to make a good prediction.) More...
 
virtual GDomNodeserialize (GDom *pDoc) const
 See the comment for GCollaborativeFilter::serialize. More...
 
virtual void train (GMatrix &data)
 Trains this recommender system. Let R be an m-by-n sparse matrix of known ratings from m users of n items. pData should contain 3 columns, and one row for each known element in R. Column 0 in pData specifies the user index from 0 to m-1, column 1 in pData specifies the item index from 0 to n-1, and column 2 in pData specifies the rating vector for that user-item pair. All attributes in pData should be continuous. More...
 
- Public Member Functions inherited from GClasses::GCollaborativeFilter
 GCollaborativeFilter ()
 
 GCollaborativeFilter (const GDomNode *pNode, GLearnerLoader &ll)
 
virtual ~GCollaborativeFilter ()
 
void basicTest (double minMSE)
 Performs a basic unit test on this collaborative filter. More...
 
double crossValidate (GMatrix &data, size_t folds, double *pOutMAE=NULL)
 This randomly assigns each rating to one of the folds. Then, for each fold, it calls train with a dataset that contains everything except for the ratings in that fold. It predicts values for the items in the fold, and returns the mean-squared difference between the predictions and the actual ratings. If pOutMAE is non-NULL, it will be set to the mean-absolute error. More...
 
GMatrixprecisionRecall (GMatrix &data, bool ideal=false)
 This divides the data into two equal-size parts. It trains on one part, and then measures the precision/recall using the other part. It returns a three-column data set with recall scores in column 0 and corresponding precision scores in column 1. The false-positive rate is in column 2. (So, if you want a precision-recall plot, just drop column 2. If you want an ROC curve, drop column 1 and swap the remaining two columns.) This method assumes the ratings range from 0 to 1, so be sure to scale the ratings to fit that range before calling this method. If ideal is true, then it will ignore your model and report the ideal results as if your model always predicted the correct rating. (This is useful because it shows the best possible results.) More...
 
GRandrand ()
 Returns a reference to the pseudo-random number generator associated with this object. More...
 
double trainAndTest (GMatrix &train, GMatrix &test, double *pOutMAE=NULL)
 This trains on the training set, and then tests on the test set. Returns the mean-squared difference between actual and target predictions. More...
 
void trainDenseMatrix (const GMatrix &data, const GMatrix *pLabels=NULL)
 Train from an m-by-n dense matrix, where m is the number of users and n is the number of items. All attributes must be continuous. Missing values are indicated with UNKNOWN_REAL_VALUE. If pLabels is non-NULL, then the labels will be appended as additional items. More...
 

Protected Attributes

GContentBasedFilterm_cbf
 
GInstanceRecommenderm_cf
 
double * m_pseudoRatingSum
 
size_t * m_ratingCounts
 
std::map< size_t, size_t > m_userMap
 
- Protected Attributes inherited from GClasses::GCollaborativeFilter
GRand m_rand
 

Additional Inherited Members

- Static Public Member Functions inherited from GClasses::GCollaborativeFilter
static double areaUnderCurve (GMatrix &data)
 Pass in the data returned by the precisionRecall function (unmodified), and this will compute the area under the ROC curve. More...
 
- Protected Member Functions inherited from GClasses::GCollaborativeFilter
GDomNodebaseDomNode (GDom *pDoc, const char *szClassName) const
 Child classes should use this in their implementation of serialize. More...
 

Constructor & Destructor Documentation

GClasses::GContentBoostedCF::GContentBoostedCF ( GArgReader  copy)
virtual GClasses::GContentBoostedCF::~GContentBoostedCF ( )
virtual

Destructor.

Member Function Documentation

virtual void GClasses::GContentBoostedCF::impute ( GVec vec,
size_t  dims 
)
virtual

pVec should be a vector of n real values, where n is the number of items/attributes/columns in the data that was used to train the model. to UNKNOWN_REAL_VALUE. This method will evaluate the known elements and impute (predict) values for the unknown elements. (The model should be trained before this method is called. Unlike the predict method, this method can operate on row-vectors that were not part of the training data.)

Implements GClasses::GCollaborativeFilter.

virtual double GClasses::GContentBoostedCF::predict ( size_t  user,
size_t  item 
)
virtual

This returns a prediction for how the specified user will rate the specified item. (The model must be trained before this method is called. Also, some values for that user and item should have been included in the training set, or else this method will have no basis to make a good prediction.)

Implements GClasses::GCollaborativeFilter.

virtual GDomNode* GClasses::GContentBoostedCF::serialize ( GDom pDoc) const
inlinevirtual
virtual void GClasses::GContentBoostedCF::train ( GMatrix data)
virtual

Trains this recommender system. Let R be an m-by-n sparse matrix of known ratings from m users of n items. pData should contain 3 columns, and one row for each known element in R. Column 0 in pData specifies the user index from 0 to m-1, column 1 in pData specifies the item index from 0 to n-1, and column 2 in pData specifies the rating vector for that user-item pair. All attributes in pData should be continuous.

Implements GClasses::GCollaborativeFilter.

Member Data Documentation

GContentBasedFilter* GClasses::GContentBoostedCF::m_cbf
protected
GInstanceRecommender* GClasses::GContentBoostedCF::m_cf
protected
double* GClasses::GContentBoostedCF::m_pseudoRatingSum
protected
size_t* GClasses::GContentBoostedCF::m_ratingCounts
protected
std::map<size_t, size_t> GClasses::GContentBoostedCF::m_userMap
protected