poismf (version 0.4.0-4)

poismf_unsafe: Poisson factorization with no input casting

Description

This is a faster version of poismf which will not make any checks or castings on its inputs. It is intended as a fast alternative when a model is to be fit multiple times with different hyperparameters, and for allowing custom-initialized factor matrices. Note that since it doesn't make any checks or conversions, passing the wrong kinds of inputs or passing inputs with mismatching dimensions will crash the R process.

For most use cases, it's recommended to use the function `poismf` instead.

Usage

poismf_unsafe(A, B, Xcsr, Xcsc, k, ...)

Value

A `poismf` model object. See the documentation for poismf for details.

Arguments

A

Initial values for the user-factor matrix of dimensions [dimA, k], assuming row-major order. Can be passed as a vector of dimension [dimA*k], or as a matrix of dimension [k, dimA]. Note that R matrices use column-major order, so if you want to pass an R matrix as initial values, you'll need to transpose it, hence the shape [k, dimA]. Recommended to initialize `~ Uniform(0.3, 0.31)`. Will be modified in-place.

B

Initial values for the item-factor matrix of dimensions [dimB, k]. See documentation about `A` for more details.

Xcsr

The `X` matrix in CSR format. Should be an object of class `Matrix::dgRMatrix`.

Xcsc

The `X` matrix in CSC format. Should be an object of class `Matrix::dgCMatrix`.

k

The number of latent factors. Must match with the dimension of `A` and `B`.

...

Other hyperparameters that can be passed to `poismf`. See the documentation for poismf for details about possible hyperparameters.

See Also

poismf

Examples

library(poismf)

### create a random sparse data frame in COO format
nrow <- 10^2 ## <- users
ncol <- 10^3 ## <- items
nnz  <- 10^4 ## <- events (agg)
set.seed(1)
X <- data.frame(
        row_ix = sample(nrow, size=nnz, replace=TRUE),
        col_ix = sample(ncol, size=nnz, replace=TRUE),
        count  = rpois(nnz, 1) + 1
     )
X <- X[!duplicated(X[, c("row_ix", "col_ix")]), ]

### convert to required format
Xcsr <- Matrix::sparseMatrix(
            i=X$row_ix, j=X$col_ix, x=X$count,
            repr="R"
)
Xcsc <- Matrix::sparseMatrix(
            i=X$row_ix, j=X$col_ix, x=X$count,
            repr="C"
)

### initialize factor matrices
k <- 5L
A <- rgamma(nrow*k, 1, 1)
B <- rgamma(ncol*k, 1, 1)

### call function
model <- poismf_unsafe(A, B, Xcsr, Xcsc, k, nthreads=1)