toSparseTorchPython: Convert the plpData in COO format into a sparse python matrix...

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Converts the standard plpData to a sparse matrix firectly into python

Usage

toSparseTorchPython(
  plpData,
  population,
  map = NULL,
  temporal = F,
  pythonExePath = NULL
)

Arguments

plpData	An object of type plpData with covariate in coo format - the patient level prediction data extracted from the CDM.
population	The population to include in the matrix
map	A covariate map (telling us the column number for covariates)
temporal	Whether to include timeId into tensor
pythonExePath	Location of python exe you want to use

Details

This function converts the covariate file from ffdf in COO format into a sparse matrix from the package Matrix

Value

Returns a list, containing the python object name of the sparse matrix, the plpData covariateRef and a data.frame named map that tells us what covariate corresponds to each column This object is a list with the following components:

data

The python object name containing a sparse matrix with the rows corresponding to each person in the plpData and the columns corresponding to the covariates.

covariateRef

The plpData covariateRef.

map

A data.frame containing the data column ids and the corresponding covariateId from covariateRef.

Examples

#TODO

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.