recosystem (version 0.5.1)

predict: Recommender Model Predictions

Description

This method is a member function of class "RecoSys" that predicts unknown entries in the rating matrix.

Prior to calling this method, model needs to be trained using member function $train().

The common usage of this method is

r = Reco()
r$train(...)
r$predict(test_data, out_pred = out_file("predict.txt")

Arguments

r

Object returned by Reco().

test_data

An object of class "DataSource" that describes the source of testing data, typically returned by function data_file(), data_memory(), or data_matrix().

out_pred

An object of class Output that specifies the output format of prediction, typically returned by function out_file(), out_memory() or out_nothing(). out_file() writes the result into a file, out_memory() exports the vector of predicted values into the return value of $predict(), and out_nothing() means the result will be neither returned nor written into a file (but computation will still be conducted).

Author

Yixuan Qiu <https://statr.me>

References

W.-S. Chin, Y. Zhuang, Y.-C. Juan, and C.-J. Lin. A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems. ACM TIST, 2015.

W.-S. Chin, Y. Zhuang, Y.-C. Juan, and C.-J. Lin. A Learning-rate Schedule for Stochastic Gradient Methods to Matrix Factorization. PAKDD, 2015.

W.-S. Chin, B.-W. Yuan, M.-Y. Yang, Y. Zhuang, Y.-C. Juan, and C.-J. Lin. LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems. Technical report, 2015.

See Also

$train()

Examples

if (FALSE) {
train_file = data_file(system.file("dat", "smalltrain.txt", package = "recosystem"))
test_file = data_file(system.file("dat", "smalltest.txt", package = "recosystem"))
r = Reco()
set.seed(123) # This is a randomized algorithm
opts_tune = r$tune(train_file)$min
r$train(train_file, out_model = NULL, opts = opts_tune)

## Write predicted values into file
out_pred = out_file(tempfile())
r$predict(test_file, out_pred)

## Return predicted values in memory
pred = r$predict(test_file, out_memory())

## If testing data are stored in memory
test_df = read.table(test_file@source, sep = " ", header = FALSE)
test_data = data_memory(test_df[, 1], test_df[, 2])
pred2 = r$predict(test_data, out_memory())

## Compare results
print(scan(out_pred@dest, n = 10))
head(pred, 10)
head(pred2, 10)

## If testing data are stored as a sparse matrix
if(require(Matrix))
{
    mat = Matrix::sparseMatrix(i = test_df[, 1], j = test_df[, 2], x = -1,
                               repr = "T", index1 = FALSE)
    test_data = data_matrix(mat)
    pred3 = r$predict(test_data, out_memory())
    print(head(pred3, 10))
}
}