generateHyperParsEffectData: Generate hyperparameter effect data.

Description Usage Arguments Value Examples

View source: R/generateHyperParsEffect.R

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, 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

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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)
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)

mlr documentation built on April 1, 2018, 12:03 p.m.