Advanced Tuning
Iterated F-Racing for mixed spaces and dependencies
The package supports a larger number of tuning algorithms, which can all be looked up and selected via TuneControl. One of the cooler algorithms is iterated F-racing from the irace package (technical description here). This not only works for arbitrary parameter types (numeric, integer, discrete, logical), but also for so-called dependent / hierarchical parameters:
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeDiscreteParam("kernel", values = c("vanilladot", "polydot", "rbfdot")),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x,
requires = quote(kernel == "rbfdot")),
makeIntegerParam("degree", lower = 2L, upper = 5L,
requires = quote(kernel == "polydot"))
)
ctrl = makeTuneControlIrace(maxExperiments = 200L)
rdesc = makeResampleDesc("Holdout")
res = tuneParams("classif.ksvm", iris.task, rdesc, par.set = ps, control = ctrl, show.info = FALSE)
print(head(as.data.frame(res$opt.path)))
#> C kernel sigma degree mmce.test.mean dob eol
#> 1 -2.8894525 polydot NA 4 0.06 1 NA
#> 2 -2.6793542 vanilladot NA NA 0.04 1 NA
#> 3 -0.7855061 rbfdot 11.049783 NA 0.68 1 NA
#> 4 1.7678978 polydot NA 5 0.14 1 NA
#> 5 9.7729840 vanilladot NA NA 0.04 1 NA
#> 6 -2.7930352 rbfdot -7.198476 NA 0.36 1 NA
#> error.message exec.time
#> 1 <NA> 0.033
#> 2 <NA> 0.027
#> 3 <NA> 0.032
#> 4 <NA> 0.030
#> 5 <NA> 0.031
#> 6 <NA> 0.034
See how we made the kernel parameters like sigma and degree dependent on the kernel
selection parameters? This approach allows you to tune parameters of multiple kernels at once,
efficiently concentrating on the ones which work best for your given data set.
Tuning across whole model spaces with ModelMultiplexer
We can now take the following example even one step further. If we use the ModelMultiplexer we can tune over different model classes at once, just as we did with the SVM kernels above.
base.learners = list(
makeLearner("classif.ksvm"),
makeLearner("classif.randomForest")
)
lrn = makeModelMultiplexer(base.learners)
Function makeModelMultiplexerParamSet offers a simple way to construct a parameter set for tuning:
The parameter names are prefixed automatically and the requires element is set, too,
to make all parameters subordinate to selected.learner.
ps = makeModelMultiplexerParamSet(lrn,
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x),
makeIntegerParam("ntree", lower = 1L, upper = 500L)
)
print(ps)
#> Type len Def
#> selected.learner discrete - -
#> classif.ksvm.sigma numeric - -
#> classif.randomForest.ntree integer - -
#> Constr Req Tunable
#> selected.learner classif.ksvm,classif.randomForest - TRUE
#> classif.ksvm.sigma -12 to 12 Y TRUE
#> classif.randomForest.ntree 1 to 500 Y TRUE
#> Trafo
#> selected.learner -
#> classif.ksvm.sigma Y
#> classif.randomForest.ntree -
rdesc = makeResampleDesc("CV", iters = 2L)
ctrl = makeTuneControlIrace(maxExperiments = 200L)
res = tuneParams(lrn, iris.task, rdesc, par.set = ps, control = ctrl, show.info = FALSE)
print(head(as.data.frame(res$opt.path)))
#> selected.learner classif.ksvm.sigma classif.randomForest.ntree
#> 1 classif.randomForest NA 273
#> 2 classif.ksvm 10.53605 NA
#> 3 classif.ksvm -11.79057 NA
#> 4 classif.ksvm 10.42478 NA
#> 5 classif.randomForest NA 394
#> 6 classif.ksvm 11.02356 NA
#> mmce.test.mean dob eol error.message exec.time
#> 1 0.04666667 1 NA <NA> 0.059
#> 2 0.68000000 1 NA <NA> 0.046
#> 3 0.52666667 1 NA <NA> 0.045
#> 4 0.68000000 1 NA <NA> 0.047
#> 5 0.04666667 1 NA <NA> 0.063
#> 6 0.68000000 1 NA <NA> 0.050
Multi-criteria evaluation and optimization
During tuning you might want to optimize multiple, potentially conflicting, performance measures simultaneously.
In the following example we aim to minimize both, the false positive and the false negative rates (fpr and fnr). We again tune the hyperparameters of an SVM (function ksvm) with a radial basis kernel and use the sonar classification task for illustration. As search strategy we choose a random search.
For all available multi-criteria tuning algorithms see TuneMultiCritControl.
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
ctrl = makeTuneMultiCritControlRandom(maxit = 30L)
rdesc = makeResampleDesc("Holdout")
res = tuneParamsMultiCrit("classif.ksvm", task = sonar.task, resampling = rdesc, par.set = ps,
measures = list(fpr, fnr), control = ctrl, show.info = FALSE)
res
#> Tune multicrit result:
#> Points on front: 2
head(as.data.frame(trafoOptPath(res$opt.path)))
#> C sigma fpr.test.mean fnr.test.mean dob eol
#> 1 2.837139e-02 0.004605846 1.00 0.00000000 1 NA
#> 2 8.161350e+00 10.073402485 1.00 0.00000000 2 NA
#> 3 2.947371e+03 0.023696559 0.15 0.03333333 3 NA
#> 4 5.020557e-01 0.279973960 1.00 0.00000000 4 NA
#> 5 8.642356e+01 47.600399172 1.00 0.00000000 5 NA
#> 6 3.661447e-04 0.715765529 1.00 0.00000000 6 NA
#> error.message exec.time
#> 1 <NA> 0.054
#> 2 <NA> 0.061
#> 3 <NA> 0.050
#> 4 <NA> 0.064
#> 5 <NA> 0.054
#> 6 <NA> 0.055
The results can be visualized with function plotTuneMultiCritResult. The plot shows the false positive and false negative rates for all parameter settings evaluated during tuning. Points on the Pareto front are slightly increased.
plotTuneMultiCritResult(res)
