parallel: bnlearn - snow/parallel package integration

In jtrecenti/bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Description

How to use the bnlearn package with the parallel computing environment provided by the snow or parallel packages.

Parallel computing for constraint-based algorithms

# load parallel and bnlearn and rsprng.
> library(parallel)
> library(bnlearn)
> cl = makeCluster(2)
# check it works.
> clusterEvalQ(cl, runif(10))
[[1]]
 [1] 0.9245585 0.1876445 0.3371175 0.2267916 0.0392876 0.9085125
 [7] 0.9041345 0.7408525 0.1537343 0.9503611

[[2]]
 [1] 0.1932651 0.8218854 0.6087155 0.9037118 0.5257906 0.8737284
 [7] 0.5225114 0.8149691 0.1671706 0.6883363
# load the data.
> data(learning.test)
# call a learning function passing the cluster object (the
# return value of the previous makeCluster() call) as a
# parameter.
> res = gs(learning.test, cluster = cl)
# note that the number of test is evenly divided between
# the two nodes of the cluster.
> clusterEvalQ(cl, test.counter())
[[1]]
[1] 47

[[2]]
[1] 42
# a few tests are still executed by this process.
> (test.counter())
[1] 4
# stop the cluster.
> stopCluster(cl)

Author(s)

Marco Scutari

jtrecenti/bnlearn documentation built on May 20, 2019, 3:16 a.m.