SPOTMisc (version 1.19.52)

startXGBCensusRun: Start hyperparameter optimization runs with spot based on US census data

Description

Runs to compare standard machine learning and deep learning models

Usage

startXGBCensusRun(
  modelList = list("xgboost"),
  runNr = "000",
  SPOTVersion = "2.11.14",
  SPOTMiscVersion = "1.19.28",
  timebudget = 3600,
  target = "age",
  cachedir = "oml.cache",
  task.type = "classif",
  nobs = 10000,
  nfactors = "high",
  nnumericals = "high",
  cardinality = "high",
  data.seed = 1,
  prop = 2/3,
  batch_size = 32,
  tuner.seed = 1,
  returnValue = "validationLoss",
  initSizeFactor = 2,
  spotModel = buildKriging,
  spotOptim = optimDE,
  lower = NULL,
  upper = NULL,
  noise = TRUE,
  OCBA = TRUE,
  OCBABudget = 3,
  multiStart = 2,
  multFun = 200,
  handleNAsMethod = handleNAsMean,
  imputeCriteriaFuns = list(is.infinite, is.na, is.nan),
  krigingTarget = "ei",
  krigingUseLambda = TRUE,
  krigingReinterpolate = FALSE,
  defaultAsStartingPoint = TRUE,
  plots = FALSE,
  Rinit = 2,
  replicates = 2,
  resDummy = FALSE,
  verbosity = 0
)

Arguments

modelList

list of models. Default: list("xgboost")

runNr

character, specifies the run number. Default: "000"

SPOTVersion

smallest package version number

SPOTMiscVersion

smallest package version number

timebudget

time budget Default: 3600 (secs)

target

target "age"

cachedir

cache dir "oml.cache"

task.type

task type "classif"

nobs

number of observations 1e4

nfactors

number of factorial variables "high"

nnumericals

number of numerical variables "high"

cardinality

cardinality "high"

data.seed

1

prop

proportion 2 / 3

batch_size

batch size (for dl) 32

tuner.seed

seed for SPOT 1

returnValue

"validationLoss"

initSizeFactor

multiplier for the initial design size 2

spotModel

buildKriging

spotOptim

optimDE

lower

NULL

upper

NULL

noise

TRUE

OCBA

TRUE

OCBABudget

3

multiStart

2

multFun

200

handleNAsMethod

handleNAsMean

imputeCriteriaFuns

list(is.infinite, is.na, is.nan)

krigingTarget

"ei"

krigingUseLambda

TRUE

krigingReinterpolate

FALSE

defaultAsStartingPoint

FALSE

plots

FALSE

Rinit

2

replicates

2

resDummy

FALSE

verbosity

0

Examples

# \donttest{
### These examples require an activated Python environment as described in
### Bartz-Beielstein, T., Rehbach, F., Sen, A., and Zaefferer, M.:
### Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT,
### June 2021. http://arxiv.org/abs/2105.14625.
PYTHON_RETICULATE <- FALSE
if(PYTHON_RETICULATE){
library("dplyr")
library("farff")
library("GGally")
library("keras")
library("tensorflow")
library("Metrics")
library("mlr")
library("reticulate")
library("rpart")
library("rpart.plot")
library("SPOT")
library("SPOTMisc")
library("tfdatasets")
library("rsample")
startXGBCensusRun(modelList=list("xgboost"), timebudget=60, plots=TRUE)
}
# }