resolution,
or discretize them yourself by always using makeDiscreteParam
in the par.set passed to tuneParams.cma_es.
Can handle numeric(vector) and integer(vector) hyperparameters, but no dependencies.
For integers the internally proposed numeric values are automatically rounded.
The sigma variance parameter is initialized to 1/4 of the span of box-constraints per
parameter dimension.GenSA.
Can handle numeric(vector) and integer(vector) hyperparameters, but no dependencies.
For integers the internally proposed numeric values are automatically rounded.irace.
All kinds of parameter types can be handled. We return the best of the final elite
candidates found by irace in the last race. Its estimated performance is the mean of all
evaluations ever done for that candidate. More information on irace can be found in the TR at
http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-004.pdf.ResampleDesc,
not a ResampleInstance.
The resampling strategy is randomly instantiated n.instances times and
these are the instances in the sense of irace (instances element of tunerConfig
in irace). Also note that irace will always
store its tuning results in a file on disk, see the package documentation for details on this
and how to change the file path.makeTuneControlCMAES(same.resampling.instance = TRUE, impute.val = NULL,
start = NULL, tune.threshold = FALSE, tune.threshold.args = list(),
log.fun = NULL, final.dw.perc = NULL, budget = NULL, ...)makeTuneControlDesign(same.resampling.instance = TRUE, impute.val = NULL,
design = NULL, tune.threshold = FALSE, tune.threshold.args = list(),
log.fun = NULL, budget = NULL)
makeTuneControlGenSA(same.resampling.instance = TRUE, impute.val = NULL,
start = NULL, tune.threshold = FALSE, tune.threshold.args = list(),
log.fun = NULL, final.dw.perc = NULL, budget = NULL, ...)
makeTuneControlGrid(same.resampling.instance = TRUE, impute.val = NULL,
resolution = 10L, tune.threshold = FALSE, tune.threshold.args = list(),
log.fun = NULL, final.dw.perc = NULL, budget = NULL)
makeTuneControlIrace(impute.val = NULL, n.instances = 100L,
show.irace.output = FALSE, tune.threshold = FALSE,
tune.threshold.args = list(), log.fun = NULL, final.dw.perc = NULL,
budget = NULL, ...)
makeTuneControlRandom(same.resampling.instance = TRUE, maxit = NULL,
tune.threshold = FALSE, tune.threshold.args = list(), log.fun = NULL,
final.dw.perc = NULL, budget = NULL)
logical(1)]
Should the same resampling instance be used for all evaluations to reduce variance?
Default is TRUE.numeric]
If something goes wrong during optimization (e.g. the learner crashes),
this value is fed back to the tuner, so the tuning algorithm does not abort.
It is not stored in the optimization path, an NA and a corresponding error message are
logged instead.
Note that this value is later multiplied by -1 for maximization measures internally, so you
need to enter a larger positive value for maximization here as well.
Default is the worst obtainable value of the performance measure you optimize for when
you aggregate by mean value, or Inf instead.
For multi-criteria optimization pass a vector of imputation values, one for each of your measures,
in the same order as your measures.list]
Named list of initial parameter values.logical(1)]
Should the threshold be tuned for the measure at hand, after each hyperparameter evaluation,
via tuneThreshold?
Only works for classification if the predict type is “prob”.
Default is FALSE.list]
Further arguments for threshold tuning that are passed down to tuneThreshold.
Default is none.function | NULL]
Function used for logging. If set to NULL, the internal default will be used.
Otherwise a function with arguments learner, resampling, measures,
par.set, control, opt.path, dob, x, y, remove.nas,
and stage is expected.
The default displays the performance measures, the time needed for evaluating,
the currently used memory and the max memory ever used before
(the latter two both taken from gc).
See the implementation for details.boolean]
If a Learner wrapped by a makeDownsampleWrapper is used, you can define the value of dw.perc which is used to train the Learner with the final parameter setting found by the tuning.
Default is NULL which will not change anything.integer(1)]
Maximum budget for tuning. This value restricts the number of function
evaluations. In case of makeTuneControlGrid this number must be identical
to the size of the grid. For makeTuneControlRandom the
budget equals the number of iterations (maxit) performed by
the random search algorithm. Within the cma_es the
budget corresponds to the product of the number of generations
(maxit) and the number of offsprings per generation
(lambda). GenSA defines the budget via
the argument max.call. However, one should note that this algorithm
does not stop its local search before its end. This behaviour might lead
to an extension of the defined budget and will result in a warning. In
irace, budget is passed to maxExperiments.data.frame]
data.frame containing the different parameter settings to be evaluated.
The columns have to be named according to the ParamSet which will be used in tune().
Proper designs can be created with generateDesign for instance.integer]
Resolution of the grid for each numeric/integer parameter in par.set.
For vector parameters, it is the resolution per dimension.
Either pass one resolution for all parameters, or a named vector.
See generateGridDesign.
Default is 10.integer(1)]
Number of random resampling instances for irace, see details.
Default is 100.logical(1)]
Show console output of irace while tuning?
Default is FALSE.integer(1) | NULL]
Number of iterations for random search.
Default is 100.TuneControl]. The specific subclass is one of
TuneControlGrid, TuneControlRandom,
TuneControlCMAES, TuneControlGenSA,
TuneControlIrace.getNestedTuneResultsOptPathDf,
getNestedTuneResultsX,
getTuneResult,
makeModelMultiplexerParamSet,
makeModelMultiplexer,
makeTuneWrapper, tuneParams,
tuneThreshold