xvalLoop: Cross-validation in clustered computing environments

In MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

Description

Use cross-validation in a clustered computing environment

Usage

xvalLoop( cluster, ... )

Arguments

cluster	Any S4-class object, used to indicate how to perform clustered computations.
...	Additional arguments used to inform the clustered computation.

Details

Cross-validiation usually involves repeated calls to the same function, but with different arguments. This provides an obvious place for using clustered computers to enhance execution. The method xval is structured to exploit this; xvalLoop provides an easy mechanism to change how xval performs cross-validation.

The idea is to write an xvalLoop method that returns a function. The function is then used to execute the cross-validation. For instance, the default method returns the function lapply, so the cross-validation is performed by using lapply. A different method might return a function that executed lapply-like functions, but sent different parts of the function to different computer nodes.

An accompanying vignette illustrates the technique in greater detail. An effective division of labor is for experienced cluster programmers to write lapply-like methods for their favored clustering environment. The user then only has to add the cluster object to the list of arguments to xval to get clustered calculations.

Value

A function taking arguments like those for lapply

Examples

## Not run: 
library(golubEsets)
data(Golub_Merge)
smallG <- Golub_Merge[200:250,]

# Evaluation on one node

lk1 <- xval(smallG, "ALL.AML", knnB, xvalMethod="LOO", group=as.integer(0))
table(lk1,smallG$ALL.AML)

# Evaluation on several nodes -- a cluster programmer might write the following...

library(snow)
setOldClass("spawnedMPIcluster")

setMethod("xvalLoop", signature( cluster = "spawnedMPIcluster"),
## use the function returned below to evalutae
## the central cross-validation loop in xval
function( cluster, ... ) {
    clusterExportEnv <- function (cl, env = .GlobalEnv)
    {
        unpackEnv <- function(env) {
            for ( name in ls(env) ) assign(name, get(name, env), .GlobalEnv )
            NULL
        }
        clusterCall(cl, unpackEnv, env)
    }
    function(X, FUN, ...) { # this gets returned to xval
        ## send all visible variables from the parent (i.e., xval) frame
        clusterExportEnv( cluster, parent.frame(1) )
        parLapply( cluster, X, FUN, ... )
    }
})

# ... and use the cluster like this...

cl <- makeCluster(2, "MPI")
clusterEvalQ(cl, library(MLInterfaces))

lk1 <- xval(smallG, "ALL.AML", knnB, xvalMethod="LOO", group=as.integer(0), cluster = cl)
table(lk1,smallG$ALL.AML)

## End(Not run)

MLInterfaces documentation built on Nov. 8, 2020, 8:14 p.m.