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

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generateHyperParsEffectData(tune.result, include.diagnostics = FALSE,
  trafo = 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.

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, and the optimization algorithm used.

Examples

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## 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)
plotHyperParsEffect(data, x = "C", y = "mmce.test.mean")

# 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)

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