Nothing
inst/doc/SPOTMiscVignette.R
In SPOTMisc: Misc Extensions for the 'SPOT' Package
## ----knitrSetup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval = FALSE------------------------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
## ---- eval = FALSE------------------------------------------------------------
# cd <project-dir>
## ---- eval = FALSE------------------------------------------------------------
# virtualenv python
## ---- eval = FALSE------------------------------------------------------------
# source python/bin/activate
## ---- eval = FALSE------------------------------------------------------------
# which python
## ---- eval = FALSE------------------------------------------------------------
# pip install numpy pandas matplotlib tensorflow tensorflow-datasets
## ---- eval=FALSE--------------------------------------------------------------
# /Users/bartz/workspace/SPOT/python/bin/python -m pip install --upgrade pip
## ---- eval = FALSE------------------------------------------------------------
# install.packages("reticulate")
## ---- eval = FALSE------------------------------------------------------------
# Sys.setenv(RETICULATE_PYTHON = "python/bin/python")
## ---- eval = FALSE------------------------------------------------------------
# reticulate::py_config()
## ---- eval = FALSE------------------------------------------------------------
# install.packages("tensorflow")
# install.packages("keras")
# library(keras)
# install_keras()
## ---- eval = FALSE------------------------------------------------------------
# install_keras(tensorflow = "gpu")
## ----SPOT, include = FALSE, eval = FALSE--------------------------------------
# ## install.packages("devtools")
# ## devtools::install_github("r-lib/devtools")
# url <- "http://owos.gm.fh-koeln.de:8055/bartz/spot.git"
# devtools::install_git(url = url)
## ---- setup, eval=FALSE-------------------------------------------------------
# library("SPOT")
# packageVersion("SPOT")
## ---- spotNoise, eval = FALSE-------------------------------------------------
# # noisy objective
# res1 <- spot(,function(x)funSphere(x)+rnorm(nrow(x)),c(-2,-3),c(1,2),
# control=list(funEvals=40,noise=TRUE))
# # noise with replicated evaluations
# res2 <- spot(,function(x)funSphere(x)+rnorm(nrow(x)),c(-2,-3),c(1,2),
# control=list(funEvals=40,noise=TRUE,replicates=2,
# designControl=list(replicates=2)))
# # and with OCBA
# res3 <- spot(,function(x)funSphere(x)+rnorm(nrow(x)),c(-2,-3),c(1,2),
# control=list(funEvals=40,noise=TRUE,replicates=2,OCBA=TRUE,OCBABudget=1,
# designControl=list(replicates=2)))
# # Check results with non-noisy function:
# funSphere(res1$xbest)
# funSphere(res2$xbest)
# funSphere(res3$xbest)
## ----braninFactor, eval=FALSE-------------------------------------------------
# braninFunctionFactor <- function (x) {
# y <- (x[2] - 5.1/(4 * pi^2) * (x[1] ^2) + 5/pi * x[1] - 6)^2 +
# 10 * (1 - 1/(8 * pi)) * cos(x[1] ) + 10
# if(x[3]==1)
# y <- y +1
# else if(x[3]==2)
# y <- y -1
# return(y)
# }
## ----vecObj, eval = FALSE-----------------------------------------------------
# objFun <- function(x){apply(x,1,braninFunctionFactor)}
## ---- spotFac, eval = FALSE---------------------------------------------------
# set.seed(1)
# res <- spot(fun=objFun,lower=c(-5,0,1),upper=c(10,15,3),
# control=list(model=buildKriging,
# types= c("numeric","numeric","factor"),
# optimizer=optimLHD))
# res$xbest
# res$ybest
## ----splotLag, eval = FALSE---------------------------------------------------
# ## run spot without log
# res <- spot(fun = funSphere,
# lower=c(0,0),
# upper=c(100,100)
# )
# ## run spot with log
# funSphereLog <- function(x){
# cbind(funSphere(x),x)
# }
# res2 <- spot(fun = funSphereLog,
# lower=c(0,0),
# upper=c(100,100)
# )
# res$logInfo
# res2$logInfo
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
# lower <- c(1e-6, 1e-6, 16,0.6, 1e-9, 10, 6,0.4,0.99,1,1e-8)
# upper <- c(0.5, 0.5, 512, 1.5, 1e-2, 50, 10,0.999,0.999,10,6e-8)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "integer", "integer", "numeric", "numeric", "integer",
# "numeric")
#
#
# kerasConf$verbose <- 0
# res <- spot(x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(funEvals=100,
# noise = TRUE,
# types = types,
# plots = TRUE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1),
# kerasConf = kerasConf)
# res01 <- res
# # save(res01, file="exp01.RData")
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
# lower <- c(1e-6, 1e-6, 16,0.6, 1e-9, 10, 6,0.4,0.99,1,1e-8)
# upper <- c(0.5, 0.5, 512, 1.5, 1e-2, 50, 10,0.999,0.999,10,6e-8)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "integer", "integer", "numeric", "numeric", "integer",
# "numeric")
#
#
# kerasConf$verbose <- 0
# Ninit <- 3 * length(lower)
# Rinit <- 3
# replicates <- 3
#
# res <- spot(x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(funEvals=20*length(lower),
# noise = TRUE,
# types = types,
# replicates=replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl=list(target="ei"),
# plots = TRUE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1),
# kerasConf = kerasConf)
# res02 <- res
# #save(res02, file="exp02.RData")
## ----run002, eval=FALSE-------------------------------------------------------
# #install.packages("~/workspace/SPOTMisc_1.2.26.tar.gz", repos=NULL, type="source")
# #install.packages("~/workspace/SPOT_2.6.2.tar.gz", repos=NULL, type="source")
# library("keras")
# library("SPOT")
# library("SPOTMisc")
# packageVersion("SPOT")
# packageVersion("SPOTMisc")
# set.seed(123)
#
# dat <- genCatsDogsData()
# kerasConf <- getKerasConf()
# kerasConf$trainData <- dat$trainData
# kerasConf$validationData <- dat$validationData
# kerasConf$testData <- dat$testData
#
# lower <- c(1e-6, 1e-6, 16,0.6, 1e-9, 10, 6,0.4,0.99,1,1e-8)
# upper <- c(0.5, 0.5, 512, 1.5, 1e-2, 50, 10,0.999,0.999,10,6e-8)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "integer", "integer", "numeric", "numeric", "integer",
# "numeric")
# ### First example: simple function call:
# x <- matrix(lower, 1,)
# funKerasTransferLearning(x, kerasConf = kerasConf)
# kerasConf$verbose <- 1
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=200,
# noise = TRUE,
# types = types,
# plots = TRUE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1),
# kerasConf = kerasConf)
# # save(res, file = "run002.RData")
## ---- eval = FALSE------------------------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist02", as.numeric(Sys.time()), ".RData"))
# ##
# ## Evaluation of the default hyperparameter setting (baseline)
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# ##
# source("~/workspace/spotmisc/R/funKerasMnist.R")
# library("SPOT")
# packageVersion("SPOT")
# source("~/workspace/SPOT/R/spot.R")
# source("~/workspace/SPOT/R/objectiveFunctionEvaluation.R")
# mnist <- getMnistData()
# # "dropout" = x[1],
# # "dropoutfact" = x[2],
# # "units" = x[3],
# # "unitsfact" = x[4],
# # "learning_rate" = x[5],
# # "epochs" = x[6],
# # "batchsize" = x[7],
# # "beta_1" = x[8],
# # "beta_2" = x[9],
# # "layers" = x[10],
# # "epsilon" = x[11]
# lower <- c(0.4, 0.9, 8, 0.5, 0.001, 4, 3, 0.9, 0.999, 2, 1e-07)
# upper <- lower
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# #
# kerasConf <- getKerasConf()
# kerasConf$resDummy <- FALSE
# kerasConf$verbose <- 0
# kerasConf$returnValue <- "validationLoss"
# kerasConf$naDummy <- 1234567890
# kerasConf$clearSession <- TRUE
# ### Example: spot call to evaluate the default design
# n <- 10
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = n,
# transformFun = transformFun,
# replicateResults = TRUE,
# types = types,
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 0,
# designControl=list(size=n)
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist02Default", as.numeric(Sys.time()), ".RData"))
## ----resKerasMnist03, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "trainingLoss"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist03", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist07, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "testLoss"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist07", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist08, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "negValidationAccuracy"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist08", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist09, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "negTrainingAccuracy"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist09", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist10, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "negTestAccuracy"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist10", as.numeric(Sys.time()),".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
#
# # Hyperparameters:
# # "dropout" = x[1],
# # "learning_rate" = x[2],
# # "epochs" = x[3],
# # "beta_1" = x[4],
# # "beta_2" = x[5],
# # "epsilon" = x[6],
# # "optimizer" = x[7]
#
# lower <- c(1e-6, 1e-6, 2, 0.8, 0.8, 1e-9, 1)
# upper <- c(0.2, 1e-2, 5, 0.99, 0.9999, 1e-3, 2)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "numeric", "factor")
#
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 1
# kerasConf$resDummy <- FALSE
# kerasConf$naDummy <- 1234567890
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=50,
# model=buildKriging,
# noise = TRUE,
# types = types,
# optimizer=optimDE,
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 1),
# kerasConf = kerasConf
# )
# save(res, file = paste0("resKerasTransferLearning04", as.numeric(Sys.time()),".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
#
# # Hyperparameters:
# # "dropout" = x[1],
# # "learning_rate" = x[2],
# # "epochs" = x[3],
# # "beta_1" = x[4],
# # "beta_2" = x[5],
# # "epsilon" = x[6],
# # "optimizer" = x[7]
#
# lower <- c(1e-6, 1e-6, 2, 0.8, 0.8, 1e-9, 1)
# upper <- c(0.2, 1e-2, 5, 0.99, 0.9999, 1e-3, 2)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "numeric", "factor")
#
# ### Example: spot call with extended verbosity and negative val accuracy:
# kerasConf$verbose <- 0
# kerasConf$returnValue <- "negValidationAccuracy"
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=50,
# model=buildKriging,
# noise = TRUE,
# types = types,
# optimizer=optimDE,
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 1),
# kerasConf = kerasConf
# )
# save(res, file = paste0("resKerasTransferLearning05", as.numeric(Sys.time()),".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
#
# # Hyperparameters:
# # "dropout" = x[1],
# # "learning_rate" = x[2],
# # "epochs" = x[3],
# # "beta_1" = x[4],
# # "beta_2" = x[5],
# # "epsilon" = x[6],
# # "optimizer" = x[7]
# replicates <- 1
# lower <- c(0.2, 1e-5, 5, 0.9, 0.999, 1e-7, 1)
# upper <- c(0.2, 1e-5, 5, 0.9, 0.999, 1e-7, 1)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "numeric", "factor")
#
# ### Example: spot call to evaluate the default design
# kerasConf$verbose <- 0
# kerasConf$returnValue <- "validationLoss"
# kerasConf$naDummy <- 1234567890
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=10,
# model=buildKriging,
# noise = TRUE,
# types = types,
# optimizer = optimDE,
# plots = FALSE,
# progress = TRUE,
# designControl=list(replicates=replicates),
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 1),
# kerasConf = kerasConf
# )
# save(res, file = paste0("resKerasTransferLearning06", as.numeric(Sys.time()),".RData"))
## ---- eval = FALSE------------------------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.8.4.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.8.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- TRUE
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# multiStart = 2,
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimDE,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity=0
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist11", as.numeric(Sys.time()), ".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# # install.packages("~/workspace/book/packages/SPOTMisc_1.8.2.tar.gz", repos = NULL, type = "source")
# # install.packages("~/workspace/book/packages/SPOT_2.8.4.tar.gz", repos = NULL, type = "source")
# library("SPOTMisc")
# if (packageVersion("SPOTMisc") < "1.8.0") stop("Please update 'SPOT'")
# library("SPOT")
# if (packageVersion("SPOT") < "2.8.4") stop("Please update 'SPOT'")
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 1-1
# # "units" = x[3]: 1-1
# # "unitsfact" = x[4]: 1-1
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6
# # "beta_1" = x[7]: 0.9--0.999
# # "beta_2" = x[8]: 0.99--0.9999
# # "layers" = x[9]: 1-1
# # "epsilon" = x[10]: 1e-8--1e-6
#
# lower <- c(0.1, 1, 1, 1, 1e-5, 3, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 1, 1, 1, 1e-2, 6, 0.999, 0.9999, 1, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 4, 6, 9)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 4, 6, 9)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# task.type <- "classif"
# nobs <- 1e4 # max: 229285
# nfactors = "high"
# nnumericals <- "high"
# cardinality = "high"
# data.seed <- 1
# tuner.seed <- 1
# timebudget <- 3600
# timeout <- timebudget/20
# data <- getCensusTrainValTestData(task.type=task.type,
# nobs=nobs,
# nfactors = nfactors,
# nnumericals = nnumericals,
# cardinality = cardinality,
# data.seed =1)
# specCensusPrep <- censusDataPrep(data=data, batch_size = 32)
#
# res <- spot(
# x = NULL,
# fun = funKerasCensus,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# multiStart = 2,
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimDE,
# modelControl = list(target = "y"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity=0
# ),
# kerasConf = kerasConf,
# specCensusPrep =specCensusPrep
# )
# save(res, file = paste0("rescs3Census01", as.numeric(Sys.time()), ".RData"))
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## ----knitrSetup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval = FALSE------------------------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
## ---- eval = FALSE------------------------------------------------------------
# cd <project-dir>
## ---- eval = FALSE------------------------------------------------------------
# virtualenv python
## ---- eval = FALSE------------------------------------------------------------
# source python/bin/activate
## ---- eval = FALSE------------------------------------------------------------
# which python
## ---- eval = FALSE------------------------------------------------------------
# pip install numpy pandas matplotlib tensorflow tensorflow-datasets
## ---- eval=FALSE--------------------------------------------------------------
# /Users/bartz/workspace/SPOT/python/bin/python -m pip install --upgrade pip
## ---- eval = FALSE------------------------------------------------------------
# install.packages("reticulate")
## ---- eval = FALSE------------------------------------------------------------
# Sys.setenv(RETICULATE_PYTHON = "python/bin/python")
## ---- eval = FALSE------------------------------------------------------------
# reticulate::py_config()
## ---- eval = FALSE------------------------------------------------------------
# install.packages("tensorflow")
# install.packages("keras")
# library(keras)
# install_keras()
## ---- eval = FALSE------------------------------------------------------------
# install_keras(tensorflow = "gpu")
## ----SPOT, include = FALSE, eval = FALSE--------------------------------------
# ## install.packages("devtools")
# ## devtools::install_github("r-lib/devtools")
# url <- "http://owos.gm.fh-koeln.de:8055/bartz/spot.git"
# devtools::install_git(url = url)
## ---- setup, eval=FALSE-------------------------------------------------------
# library("SPOT")
# packageVersion("SPOT")
## ---- spotNoise, eval = FALSE-------------------------------------------------
# # noisy objective
# res1 <- spot(,function(x)funSphere(x)+rnorm(nrow(x)),c(-2,-3),c(1,2),
# control=list(funEvals=40,noise=TRUE))
# # noise with replicated evaluations
# res2 <- spot(,function(x)funSphere(x)+rnorm(nrow(x)),c(-2,-3),c(1,2),
# control=list(funEvals=40,noise=TRUE,replicates=2,
# designControl=list(replicates=2)))
# # and with OCBA
# res3 <- spot(,function(x)funSphere(x)+rnorm(nrow(x)),c(-2,-3),c(1,2),
# control=list(funEvals=40,noise=TRUE,replicates=2,OCBA=TRUE,OCBABudget=1,
# designControl=list(replicates=2)))
# # Check results with non-noisy function:
# funSphere(res1$xbest)
# funSphere(res2$xbest)
# funSphere(res3$xbest)
## ----braninFactor, eval=FALSE-------------------------------------------------
# braninFunctionFactor <- function (x) {
# y <- (x[2] - 5.1/(4 * pi^2) * (x[1] ^2) + 5/pi * x[1] - 6)^2 +
# 10 * (1 - 1/(8 * pi)) * cos(x[1] ) + 10
# if(x[3]==1)
# y <- y +1
# else if(x[3]==2)
# y <- y -1
# return(y)
# }
## ----vecObj, eval = FALSE-----------------------------------------------------
# objFun <- function(x){apply(x,1,braninFunctionFactor)}
## ---- spotFac, eval = FALSE---------------------------------------------------
# set.seed(1)
# res <- spot(fun=objFun,lower=c(-5,0,1),upper=c(10,15,3),
# control=list(model=buildKriging,
# types= c("numeric","numeric","factor"),
# optimizer=optimLHD))
# res$xbest
# res$ybest
## ----splotLag, eval = FALSE---------------------------------------------------
# ## run spot without log
# res <- spot(fun = funSphere,
# lower=c(0,0),
# upper=c(100,100)
# )
# ## run spot with log
# funSphereLog <- function(x){
# cbind(funSphere(x),x)
# }
# res2 <- spot(fun = funSphereLog,
# lower=c(0,0),
# upper=c(100,100)
# )
# res$logInfo
# res2$logInfo
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
# lower <- c(1e-6, 1e-6, 16,0.6, 1e-9, 10, 6,0.4,0.99,1,1e-8)
# upper <- c(0.5, 0.5, 512, 1.5, 1e-2, 50, 10,0.999,0.999,10,6e-8)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "integer", "integer", "numeric", "numeric", "integer",
# "numeric")
#
#
# kerasConf$verbose <- 0
# res <- spot(x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(funEvals=100,
# noise = TRUE,
# types = types,
# plots = TRUE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1),
# kerasConf = kerasConf)
# res01 <- res
# # save(res01, file="exp01.RData")
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
# lower <- c(1e-6, 1e-6, 16,0.6, 1e-9, 10, 6,0.4,0.99,1,1e-8)
# upper <- c(0.5, 0.5, 512, 1.5, 1e-2, 50, 10,0.999,0.999,10,6e-8)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "integer", "integer", "numeric", "numeric", "integer",
# "numeric")
#
#
# kerasConf$verbose <- 0
# Ninit <- 3 * length(lower)
# Rinit <- 3
# replicates <- 3
#
# res <- spot(x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(funEvals=20*length(lower),
# noise = TRUE,
# types = types,
# replicates=replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl=list(target="ei"),
# plots = TRUE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1),
# kerasConf = kerasConf)
# res02 <- res
# #save(res02, file="exp02.RData")
## ----run002, eval=FALSE-------------------------------------------------------
# #install.packages("~/workspace/SPOTMisc_1.2.26.tar.gz", repos=NULL, type="source")
# #install.packages("~/workspace/SPOT_2.6.2.tar.gz", repos=NULL, type="source")
# library("keras")
# library("SPOT")
# library("SPOTMisc")
# packageVersion("SPOT")
# packageVersion("SPOTMisc")
# set.seed(123)
#
# dat <- genCatsDogsData()
# kerasConf <- getKerasConf()
# kerasConf$trainData <- dat$trainData
# kerasConf$validationData <- dat$validationData
# kerasConf$testData <- dat$testData
#
# lower <- c(1e-6, 1e-6, 16,0.6, 1e-9, 10, 6,0.4,0.99,1,1e-8)
# upper <- c(0.5, 0.5, 512, 1.5, 1e-2, 50, 10,0.999,0.999,10,6e-8)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "integer", "integer", "numeric", "numeric", "integer",
# "numeric")
# ### First example: simple function call:
# x <- matrix(lower, 1,)
# funKerasTransferLearning(x, kerasConf = kerasConf)
# kerasConf$verbose <- 1
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=200,
# noise = TRUE,
# types = types,
# plots = TRUE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1),
# kerasConf = kerasConf)
# # save(res, file = "run002.RData")
## ---- eval = FALSE------------------------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist02", as.numeric(Sys.time()), ".RData"))
# ##
# ## Evaluation of the default hyperparameter setting (baseline)
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# ##
# source("~/workspace/spotmisc/R/funKerasMnist.R")
# library("SPOT")
# packageVersion("SPOT")
# source("~/workspace/SPOT/R/spot.R")
# source("~/workspace/SPOT/R/objectiveFunctionEvaluation.R")
# mnist <- getMnistData()
# # "dropout" = x[1],
# # "dropoutfact" = x[2],
# # "units" = x[3],
# # "unitsfact" = x[4],
# # "learning_rate" = x[5],
# # "epochs" = x[6],
# # "batchsize" = x[7],
# # "beta_1" = x[8],
# # "beta_2" = x[9],
# # "layers" = x[10],
# # "epsilon" = x[11]
# lower <- c(0.4, 0.9, 8, 0.5, 0.001, 4, 3, 0.9, 0.999, 2, 1e-07)
# upper <- lower
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# #
# kerasConf <- getKerasConf()
# kerasConf$resDummy <- FALSE
# kerasConf$verbose <- 0
# kerasConf$returnValue <- "validationLoss"
# kerasConf$naDummy <- 1234567890
# kerasConf$clearSession <- TRUE
# ### Example: spot call to evaluate the default design
# n <- 10
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = n,
# transformFun = transformFun,
# replicateResults = TRUE,
# types = types,
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 0,
# designControl=list(size=n)
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist02Default", as.numeric(Sys.time()), ".RData"))
## ----resKerasMnist03, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "trainingLoss"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist03", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist07, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "testLoss"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist07", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist08, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "negValidationAccuracy"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist08", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist09, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "negTrainingAccuracy"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist09", as.numeric(Sys.time()),".RData"))
## ----resKerasMnist10, eval=FALSE----------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.7.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.6.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# kerasConf$returnValue <- "negTestAccuracy"
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimLBFGSB,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist10", as.numeric(Sys.time()),".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
#
# # Hyperparameters:
# # "dropout" = x[1],
# # "learning_rate" = x[2],
# # "epochs" = x[3],
# # "beta_1" = x[4],
# # "beta_2" = x[5],
# # "epsilon" = x[6],
# # "optimizer" = x[7]
#
# lower <- c(1e-6, 1e-6, 2, 0.8, 0.8, 1e-9, 1)
# upper <- c(0.2, 1e-2, 5, 0.99, 0.9999, 1e-3, 2)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "numeric", "factor")
#
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 1
# kerasConf$resDummy <- FALSE
# kerasConf$naDummy <- 1234567890
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=50,
# model=buildKriging,
# noise = TRUE,
# types = types,
# optimizer=optimDE,
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 1),
# kerasConf = kerasConf
# )
# save(res, file = paste0("resKerasTransferLearning04", as.numeric(Sys.time()),".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
#
# # Hyperparameters:
# # "dropout" = x[1],
# # "learning_rate" = x[2],
# # "epochs" = x[3],
# # "beta_1" = x[4],
# # "beta_2" = x[5],
# # "epsilon" = x[6],
# # "optimizer" = x[7]
#
# lower <- c(1e-6, 1e-6, 2, 0.8, 0.8, 1e-9, 1)
# upper <- c(0.2, 1e-2, 5, 0.99, 0.9999, 1e-3, 2)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "numeric", "factor")
#
# ### Example: spot call with extended verbosity and negative val accuracy:
# kerasConf$verbose <- 0
# kerasConf$returnValue <- "negValidationAccuracy"
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=50,
# model=buildKriging,
# noise = TRUE,
# types = types,
# optimizer=optimDE,
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 1),
# kerasConf = kerasConf
# )
# save(res, file = paste0("resKerasTransferLearning05", as.numeric(Sys.time()),".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# library("SPOTMisc")
# library("SPOT")
# kerasConf <- getKerasConf()
#
# # Hyperparameters:
# # "dropout" = x[1],
# # "learning_rate" = x[2],
# # "epochs" = x[3],
# # "beta_1" = x[4],
# # "beta_2" = x[5],
# # "epsilon" = x[6],
# # "optimizer" = x[7]
# replicates <- 1
# lower <- c(0.2, 1e-5, 5, 0.9, 0.999, 1e-7, 1)
# upper <- c(0.2, 1e-5, 5, 0.9, 0.999, 1e-7, 1)
# types <- c("numeric", "numeric", "integer", "numeric", "numeric",
# "numeric", "factor")
#
# ### Example: spot call to evaluate the default design
# kerasConf$verbose <- 0
# kerasConf$returnValue <- "validationLoss"
# kerasConf$naDummy <- 1234567890
# res <- spot(x = NULL,
# fun = funKerasTransferLearning,
# lower = lower,
# upper = upper,
# control = list(funEvals=10,
# model=buildKriging,
# noise = TRUE,
# types = types,
# optimizer = optimDE,
# plots = FALSE,
# progress = TRUE,
# designControl=list(replicates=replicates),
# seedFun = 1,
# seedSPOT = 1,
# verbosity = 1),
# kerasConf = kerasConf
# )
# save(res, file = paste0("resKerasTransferLearning06", as.numeric(Sys.time()),".RData"))
## ---- eval = FALSE------------------------------------------------------------
# install.packages(c(
# "benchmarkme", "callr", "emoa", "ggsci", "jsonlite", "keras", "magrittr", "mlr", "plotly", "RColorBrewer", "reticulate", "rpart.plot", "sensitivity", "smoof", "tensorflow", "tfdatasets"))
# install.packages(
# "~/workspace/spotmisc/packages/SPOT_2.8.4.tar.gz",
# repos = NULL,
# type = "source"
# )
# install.packages(
# "~/workspace/spotmisc/packages/SPOTMisc_1.8.0.tar.gz",
# repos = NULL,
# type = "source"
# )
# library("SPOTMisc")
# packageVersion("SPOTMisc")
# library("SPOT")
# packageVersion("SPOT")
#
# mnist <- getMnistData()
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 0.85--0.95
# # "units" = x[3]: 7-9 (128--512)
# # "unitsfact" = x[4]: 0.5--1.5
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6 (10--50)
# # "batchsize" = x[7]: 6-9 (64--512)
# # "beta_1" = x[8]: 0.9--0.999
# # "beta_2" = x[9]: 0.99--0.9999
# # "layers" = x[10]: 1--5
# # "epsilon" = x[11]: 1e-8--1e-8
# lower <- c(0.1, 0.85, 7, 0.5, 1e-5, 3, 6, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 0.95, 9, 1.5, 1e-2, 6, 9, 0.999, 0.9999, 5, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 6, 7)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 6, 7)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- TRUE
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# res <- spot(
# x = NULL,
# fun = funKerasMnist,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# multiStart = 2,
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimDE,
# modelControl = list(target = "ei"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity=0
# ),
# kerasConf = kerasConf,
# data = mnist
# )
# save(res, file = paste0("resKerasMnist11", as.numeric(Sys.time()), ".RData"))
## ---- eval=FALSE--------------------------------------------------------------
# # install.packages("~/workspace/book/packages/SPOTMisc_1.8.2.tar.gz", repos = NULL, type = "source")
# # install.packages("~/workspace/book/packages/SPOT_2.8.4.tar.gz", repos = NULL, type = "source")
# library("SPOTMisc")
# if (packageVersion("SPOTMisc") < "1.8.0") stop("Please update 'SPOT'")
# library("SPOT")
# if (packageVersion("SPOT") < "2.8.4") stop("Please update 'SPOT'")
#
# # "dropout" = x[1]: 0.1--0.5
# # "dropoutfact" = x[2]: 1-1
# # "units" = x[3]: 1-1
# # "unitsfact" = x[4]: 1-1
# # "learning_rate" = x[5]: 1e-5--1e-2
# # "epochs" = x[6]: 3-6
# # "beta_1" = x[7]: 0.9--0.999
# # "beta_2" = x[8]: 0.99--0.9999
# # "layers" = x[9]: 1-1
# # "epsilon" = x[10]: 1e-8--1e-6
#
# lower <- c(0.1, 1, 1, 1, 1e-5, 3, 0.9, 0.99, 1, 1e-8)
# upper <- c(0.5, 1, 1, 1, 1e-2, 6, 0.999, 0.9999, 1, 1e-6)
# types <- rep("numeric", length(lower))
# types[c(3, 4, 6, 9)] <- "integer"
# f2 <- function(x) {
# 2 ^ x
# }
# transformFun <- rep("identity", length(lower))
# transformFun[c(3, 4, 6, 9)] <- "f2"
# kerasConf <- getKerasConf()
# kerasConf$verbose <- 0
# kerasConf$resDummy <- FALSE
# Ninit <- 2 * length(lower)
# Rinit <- 2
# replicates <- 2
#
# task.type <- "classif"
# nobs <- 1e4 # max: 229285
# nfactors = "high"
# nnumericals <- "high"
# cardinality = "high"
# data.seed <- 1
# tuner.seed <- 1
# timebudget <- 3600
# timeout <- timebudget/20
# data <- getCensusTrainValTestData(task.type=task.type,
# nobs=nobs,
# nfactors = nfactors,
# nnumericals = nnumericals,
# cardinality = cardinality,
# data.seed =1)
# specCensusPrep <- censusDataPrep(data=data, batch_size = 32)
#
# res <- spot(
# x = NULL,
# fun = funKerasCensus,
# lower = lower,
# upper = upper,
# control = list(
# funEvals = 20 * length(lower),
# multiStart = 2,
# noise = TRUE,
# types = types,
# transformFun = transformFun,
# replicates = replicates,
# designControl = list(replicates = Rinit,
# size = Ninit),
# model = buildKriging,
# optimizer = optimDE,
# modelControl = list(target = "y"),
# plots = FALSE,
# progress = TRUE,
# seedFun = 1,
# seedSPOT = 1,
# verbosity=0
# ),
# kerasConf = kerasConf,
# specCensusPrep =specCensusPrep
# )
# save(res, file = paste0("rescs3Census01", as.numeric(Sys.time()), ".RData"))
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