getMlConfig: get ml config for keras on census

In SPOTMisc: Misc Extensions for the 'SPOT' Package

get ml config for keras on census

Description

get ml config for keras on census

Usage

getMlConfig(
  target,
  model,
  data,
  task.type,
  nobs,
  nfactors,
  nnumericals,
  cardinality,
  data.seed,
  prop
)

Arguments

target	character "age" or "income_class"
model	character model name, e.g., "dl"
data	data, e.g., from getDataCensus
task.type	"classif" (character)
nobs	number of observations (numerical), max 229285. Default: 1e4
nfactors	(character), e.g., "high"
nnumericals	(character), e.g., "high"
cardinality	(character), e.g., "high"
data.seed	(numerical) seed
prop	(numerical) split proportion (train, vals,test)

Value

cfg (list)

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){
target <- "age"
task.type <- "classif"
nobs <- 1e2
nfactors <- "high"
nnumericals <- "high"
cardinality <- "high"
data.seed <- 1
cachedir <- "oml.cache"
model <- "ranger"

dfCensus <- getDataCensus(
task.type = task.type,
nobs = nobs,
nfactors = nfactors,
nnumericals = nnumericals,
cardinality = cardinality,
data.seed = data.seed,
cachedir = cachedir,
target = target)

cfg <- getMlConfig(
target = target,
model = model,
data = dfCensus,
task.type = task.type,
nobs = nobs,
nfactors = nfactors,
nnumericals = nnumericals,
cardinality = cardinality,
data.seed = data.seed,
prop= 2/3)
}

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