R-RerF: Timed tests
In rerf: Randomer Forest

To perform these tests, make sure you're in the correct directory with the correct version of R-RerF installed and run the below code chunk.

require(rmarkdown)
require(knitr)
require(devtools)

opts_chunk$set(cache=FALSE,warning=FALSE,message=FALSE)

rmarkdown::render("test-Times.Rmd", output_format = "html_document")

RerF time tests

Timed tests between different versions of the RerF code that live across various git-branches are performed below.

Setup library and test data

## sandbox the install location
dev_mode(on = TRUE) 
## install from version 1.1.3 which is the CRAN version as of 20181005.
install_github('neurodata/R-RerF', ref = 'v1.1.3', local = FALSE)
require('rerf')

times <- list()
data(mnist)

## Get a random subsample, 100 each of 3's and 5's 
set.seed(317)
threes <- sample(which(mnist$Ytrain %in% 3), 100)
fives  <- sample(which(mnist$Ytrain %in% 5), 100)
numsub <- c(threes, fives)

Ytrain <- mnist$Ytrain[numsub]
Xtrain <- mnist$Xtrain[numsub,]
Ytest <- mnist$Ytest[mnist$Ytest %in% c(3,5)]
Xtest <- mnist$Xtest[mnist$Ytest %in% c(3,5),]

# p is number of dimensions, d is the number of random features to evaluate, iw is image width, ih is image height, patch.min is min width of square patch to sample pixels from, and patch.max is the max width of square patch
p <- ncol(Xtrain)
d <- ceiling(sqrt(p))
iw <- sqrt(p)
ih <- iw
patch.min <- 1L
patch.max <- 5L

Timing v1.1.3

invisible(gc())
startTime <- Sys.time()

forest <- RerF(Xtrain, Ytrain, num.cores = 1L, 
               mat.options = list(p = p, d = d, random.matrix = "image-patch", iw = iw, ih = ih, 
                                  patch.min = patch.min, patch.max = patch.max), 
               seed = 1L, rfPack = FALSE)
stopTime <- Sys.time()

times$cran <- stopTime - startTime

Timing branch staging @3ea880184ed3f593ed0720f4f0556c9f0b9f1375

detach('package:rerf', unload = TRUE)
install_github('neurodata/R-RerF', ref = '3ea880184ed3f593ed0720f4f0556c9f0b9f1375', local = FALSE)
require('rerf')

invisible(gc())
startTime <- Sys.time()
forest <- RerF(Xtrain, Ytrain, num.cores = 1L, 
               mat.options = list(p = p, d = d, random.matrix = "image-patch", iw = iw, ih = ih, 
                                  patch.min = patch.min, patch.max = patch.max), seed = 1L)
stopTime <- Sys.time()
times$staging <- stopTime - startTime

Timing branch RandMat-split @981db221aa4e6cf269a2e208878151d204413eb9

## install from branch RandMat-split.
detach('package:rerf', unload = TRUE)
install_github('neurodata/R-RerF', ref = '981db221aa4e6cf269a2e208878151d204413eb9', local = FALSE)
require('rerf')

invisible(gc())
startTime <- Sys.time()
forest <- RerF(Xtrain, Ytrain, num.cores = 1L, FUN = RandMatImagePatch,
             paramList = list(p = p, d = d, iw = iw, ih = ih, 
                              pwMin = patch.min, pwMax = patch.max), 
             seed = 1L)
stopTime <- Sys.time()
times$randMatSplit <- stopTime - startTime

dev_mode(on = FALSE)

kable(data.frame(times), format = 'markdown')

RandMat* time tests

runs <- list()
require(microbenchmark)
## below is the output of RandMat with mat.options
## from commit 73b896ff053537ee23d82b9debee054171b1c41b
## with set.seed(317) and RcppZiggurat::zsetseed(14)
## for comparison to the new version of RandMat*
#mat.options <- list(p = 5, d = 3, "binary", rho = 0.25, prob = 0.5)
rBinary <- structure(c(3, 2, 3, 2, 1, 2, 2, 3, 1, -1, -1, 1), .Dim = 4:3)

## sandbox the install location
dev_mode(on = TRUE) 

## install from version 1.1.3 which is the CRAN version as of 20181005.
install_github('neurodata/R-RerF', ref = 'v1.1.3', local = FALSE, force = TRUE)
require('rerf')


opt1 <- list(p = 5, d = 3, random.matrix = "binary", rho = 0.25, prob = 0.5)

runs$cran <- microbenchmark(run1 = RandMat(opt1))

## install from branch RandMat-split
detach('package:rerf', unload = TRUE)
install_github('neurodata/R-RerF', ref = 'RandMat-split', local = FALSE, force = TRUE)
require('rerf')

runs$randmat <- microbenchmark(run2 = RandMatBinary(p = 5, d = 3, sparsity = 0.25, prob = 0.5))

dev_mode(on = FALSE)