addProblem | R Documentation |
Problems may consist of up to two parts: A static, immutable part (data
in addProblem
)
and a dynamic, stochastic part (fun
in addProblem
).
For example, for statistical learning problems a data frame would be the static problem part while
a resampling function would be the stochastic part which creates problem instance.
This instance is then typically passed to a learning algorithm like a wrapper around a statistical model
(fun
in addAlgorithm
).
This function serialize all components to the file system and registers the problem in the ExperimentRegistry
.
removeProblem
removes all jobs from the registry which depend on the specific problem.
reg$problems
holds the IDs of already defined problems.
addProblem(
name,
data = NULL,
fun = NULL,
seed = NULL,
cache = FALSE,
reg = getDefaultRegistry()
)
removeProblems(name, reg = getDefaultRegistry())
name |
[ |
data |
[ |
fun |
[ |
seed |
[ |
cache |
[ |
reg |
[ |
[Problem
]. Object of class “Problem” (invisibly).
Algorithm
, addExperiments
tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)
addProblem("p1", fun = function(job, data) data, reg = tmp)
addProblem("p2", fun = function(job, data) job, reg = tmp)
addAlgorithm("a1", fun = function(job, data, instance) instance, reg = tmp)
addExperiments(repls = 2, reg = tmp)
# List problems, algorithms and job parameters:
tmp$problems
tmp$algorithms
getJobPars(reg = tmp)
# Remove one problem
removeProblems("p1", reg = tmp)
# List problems and algorithms:
tmp$problems
tmp$algorithms
getJobPars(reg = tmp)
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