startXGBCensusRun: Start hyperparameter optimization runs with spot based on US...
In SPOTMisc: Misc Extensions for the 'SPOT' Package

startXGBCensusRun

R Documentation

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


### 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)
}

SPOTMisc documentation built on Sept. 5, 2022, 5:06 p.m.