R: Add Experiments to the Registry

addExperiments {batchtools} | R Documentation

## Add Experiments to the Registry

### Description

Adds experiments (parametrized combinations of problems with algorithms) to the registry and thereby defines batch jobs.

If multiple problem designs or algorithm designs are provided, they are combined via the Cartesian product. E.g., if you have two problems `p1` and `p2` and three algorithms `a1`, `a2` and `a3`, `addExperiments` creates experiments for all parameters for the combinations `(p1, a1)`, `(p1, a2)`, `(p1, a3)`, `(p2, a1)`, `(p2, a2)` and `(p2, a3)`.

### Usage

    addExperiments(prob.designs = NULL, algo.designs = NULL, repls = 1L,
      combine = "crossprod", reg = getDefaultRegistry())

### Arguments

`prob.designs` | [named list of `data.frame`] Named list of data frames (or `data.table`). The name must match the problem name while the column names correspond to parameters of the problem. If `NULL`, experiments for all defined problems without any parameters are added.
`algo.designs` | [named list of `data.table` or `data.frame`] Named list of data frames (or `data.table`). The name must match the algorithm name while the column names correspond to parameters of the algorithm. If `NULL`, experiments for all defined algorithms without any parameters are added.
`repls` | [`integer(1)`] Number of replications for each experiment.
`combine` | [`character(1)`] How to combine the rows of a single problem design with the rows of a single algorithm design? Default is “crossprod” which combines each row of the problem design which each row of the algorithm design in a cross-product fashion. Set to “bind” to just `cbind` the tables of problem and algorithm designs where the shorter table is repeated if necessary.
`reg` | [`ExperimentRegistry`] Registry. If not explicitly passed, uses the last created registry.

### Value

[`data.table`] with ids of added jobs stored in column “job.id”.

### Note

R's `data.frame` converts character vectors to factors by default which frequently resulted in problems using `addExperiments`. Therefore, this function will warn about factor variables if the following conditions hold:

1. The design is passed as a `data.frame`, not a `data.table` or `tibble`.

2. The option “stringsAsFactors” is not set or set to `TRUE`.

### See Also

Other Experiment: `removeExperiments`, `summarizeExperiments`

### Examples

    tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)

    # add first problem
    fun = function(job, data, n, mean, sd, ...) rnorm(n, mean = mean, sd = sd)
    addProblem("rnorm", fun = fun, reg = tmp)

    # add second problem
    fun = function(job, data, n, lambda, ...) rexp(n, rate = lambda)
    addProblem("rexp", fun = fun, reg = tmp)

    # add first algorithm
    fun = function(instance, method, ...) if (method == "mean") mean(instance) else median(instance)
    addAlgorithm("average", fun = fun, reg = tmp)

    # add second algorithm
    fun = function(instance, ...) sd(instance)
    addAlgorithm("deviation", fun = fun, reg = tmp)

    # define problem and algorithm designs
    prob.designs = algo.designs = list()
    prob.designs$rnorm = CJ(n = 100, mean = -1:1, sd = 1:5)
    prob.designs$rexp = data.table(n = 100, lambda = 1:5)
    algo.designs$average = data.table(method = c("mean", "median"))
    algo.designs$deviation = data.table()

    # add experiments and submit
    addExperiments(prob.designs, algo.designs, reg = tmp)

    # check what has been created
    summarizeExperiments(reg = tmp)
    unwrap(getJobPars(reg = tmp))

[Package batchtools version 0.9.11 Index]