R: Generate hyperparameter effect data.

generateHyperParsEffectData {mlr} | R Documentation

## Generate hyperparameter effect data.

### Description

Generate cleaned hyperparameter effect data from a tuning result or from a nested cross-validation tuning result. The object returned can be used for custom visualization or passed downstream to an out of the box mlr method, plotHyperParsEffect.

### Usage

    generateHyperParsEffectData(tune.result, include.diagnostics = FALSE,
      trafo = FALSE, partial.dep = FALSE)

### Arguments

`tune.result` | (TuneResult | ResampleResult) Result of tuneParams (or resample ONLY when used for nested cross-validation). The tuning result (or results if the output is from nested cross-validation), also containing the optimizer results. If nested CV output is passed, each element in the list will be considered a separate run, and the data from each run will be included in the dataframe within the returned `HyperParsEffectData`.
`include.diagnostics` | (`logical(1)`) Should diagnostic info (eol and error msg) be included? Default is `FALSE`.
`trafo` | (`logical(1)`) Should the units of the hyperparameter path be converted to the transformed scale? This is only useful when trafo was used to create the path. Default is `FALSE`.
`partial.dep` | (`logical(1)`) Should partial dependence be requested based on converting to reg task? This sets a flag so that we know to use partial dependence downstream. This should most likely be set to `TRUE` if 2 or more hyperparameters were tuned simultaneously. Partial dependence should always be requested when more than 2 hyperparameters were tuned simultaneously. Setting to `TRUE` will cause plotHyperParsEffect to automatically plot partial dependence when called downstream. Default is `FALSE`.

### Value

(`HyperParsEffectData`) Object containing the hyperparameter effects dataframe, the tuning performance measures used, the hyperparameters used, a flag for including diagnostic info, a flag for whether nested cv was used, a flag for whether partial dependence should be generated, and the optimization algorithm used.

### Examples

    ## Not run:
    # 3-fold cross validation
    ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4)))
    ctrl = makeTuneControlGrid()
    rdesc = makeResampleDesc("CV", iters = 3L)
    res = tuneParams("classif.ksvm", task = pid.task, resampling = rdesc,
    par.set = ps, control = ctrl)
    data = generateHyperParsEffectData(res)
    plt = plotHyperParsEffect(data, x = "C", y = "mmce.test.mean")
    plt + ylab("Misclassification Error")

    # nested cross validation
    ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4)))
    ctrl = makeTuneControlGrid()
    rdesc = makeResampleDesc("CV", iters = 3L)
    lrn = makeTuneWrapper("classif.ksvm", control = ctrl,
                          resampling = rdesc, par.set = ps)
    res = resample(lrn, task = pid.task, resampling = cv2,
                   extract = getTuneResult)
    data = generateHyperParsEffectData(res)
    plotHyperParsEffect(data, x = "C", y = "mmce.test.mean", plot.type = "line")

    ## End(Not run)

[Package mlr version 2.14.0 Index]