IIterativeModel Interface Reference

Interface representing iteratively trained models. More...

Inheritance diagram for IIterativeModel:
IRecommender BPR_Linear MF FactorWiseMatrixFactorization MatrixFactorization UserItemBaseline BPRMF WRMF BiasedMatrixFactorization KNN ItemKNN UserKNN ItemAttributeKNN ItemKNNCosine ItemKNNPearson UserAttributeKNN UserKNNCosine UserKNNPearson

List of all members.

Public Member Functions

bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction can be made for a given user-item combination.
double ComputeFit ()
 Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
void Iterate ()
 Run one iteration (= pass over the training data).
void LoadModel (string filename)
 Get the model parameters from a file.
double Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
void SaveModel (string filename)
 Save the model parameters to a file.
string ToString ()
 Return a string representation of the recommender.
void Train ()
 Learn the model parameters of the recommender from the training data.

Properties

uint NumIter [get, set]
 Number of iterations to run the training.

Detailed Description

Interface representing iteratively trained models.


Member Function Documentation

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

Check whether a useful prediction can be made for a given user-item combination.

Parameters:
user_id the user ID
item_id the item ID
Returns:
true if a useful prediction can be made, false otherwise

Implemented in Ensemble, ItemRecommender, BiPolarSlopeOne, GlobalAverage, ItemAverage, RatingPredictor, SlopeOne, and UserAverage.

double ComputeFit (  ) 

Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.

Returns:
the fit on the training data according to the optimization criterion; -1 if not implemented

Implemented in BPR_Linear, BPRMF, MF, WRMF, FactorWiseMatrixFactorization, MatrixFactorization, and UserItemBaseline.

void Iterate (  ) 

Run one iteration (= pass over the training data).

Implemented in BPR_Linear, BPRMF, MF, WRMF, BiasedMatrixFactorization, FactorWiseMatrixFactorization, MatrixFactorization, and UserItemBaseline.

void LoadModel ( string  filename  )  [inherited]
double Predict ( int  user_id,
int  item_id 
) [inherited]

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

Implemented in Ensemble, WeightedEnsemble, BPR_Linear, BPRMF, ItemKNN, ItemRecommender, MF, MostPopular, Random, UserKNN, WeightedItemKNN, WeightedUserKNN, Zero, BiasedMatrixFactorization, BiPolarSlopeOne, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, RatingPredictor, SlopeOne, UserAverage, UserItemBaseline, and UserKNN.

void SaveModel ( string  filename  )  [inherited]
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 BPR_Linear, BPRMF, ItemAttributeKNN, ItemKNN, ItemRecommender, UserAttributeKNN, UserKNN, WeightedItemKNN, WeightedUserKNN, WRMF, BiasedMatrixFactorization, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, RatingPredictor, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.


Property Documentation

uint NumIter [get, set]

Number of iterations to run the training.

Implemented in BPR_Linear, MF, FactorWiseMatrixFactorization, MatrixFactorization, and UserItemBaseline.


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