Nothing
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$set(cache=FALSE,warning=FALSE,message=FALSE) rmarkdown::render("test-Times.Rmd", output_format = "html_document")
Timed tests between different versions of the RerF
code that live across
various git-branches are performed below.
## sandbox the install location dev_mode(on = TRUE) ## install from version 1.1.3 which is the CRAN version as of 20181005. install_github('neurodata/R-RerF', ref = 'v1.1.3', local = FALSE) require('rerf')
times <- list() data(mnist) ## Get a random subsample, 100 each of 3's and 5's set.seed(317) threes <- sample(which(mnist$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$Ytest %in% c(3,5),] # p is number of dimensions, d is the number of random features to evaluate, iw is image width, ih is image height, patch.min is min width of square patch to sample pixels from, and patch.max is the max width of square patch p <- ncol(Xtrain) d <- ceiling(sqrt(p)) iw <- sqrt(p) ih <- iw patch.min <- 1L patch.max <- 5L
invisible(gc()) startTime <- Sys.time() forest <- RerF(Xtrain, Ytrain, num.cores = 1L, mat.options = list(p = p, d = d, random.matrix = "image-patch", iw = iw, ih = ih, patch.min = patch.min, patch.max = patch.max), seed = 1L, rfPack = FALSE) stopTime <- Sys.time() times$cran <- stopTime - startTime
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$staging <- stopTime - startTime
## install from branch RandMat-split. detach('package:rerf', unload = TRUE) install_github('neurodata/R-RerF', ref = '981db221aa4e6cf269a2e208878151d204413eb9', local = FALSE) require('rerf')
invisible(gc()) startTime <- Sys.time() forest <- RerF(Xtrain, Ytrain, num.cores = 1L, FUN = RandMatImagePatch, paramList = list(p = p, d = d, iw = iw, ih = ih, pwMin = patch.min, pwMax = patch.max), seed = 1L) stopTime <- Sys.time() times$randMatSplit <- stopTime - startTime dev_mode(on = FALSE)
kable(data.frame(times), format = 'markdown')
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)
runs
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