Performs tournament surrogate model filtering. A surrogate model is used, as described in the parent class FiltorSurrogate.
Selects individuals from a tournament by taking the top per_tournament individuals, according to surrogate_selector and
as predicted by surrogate_learner, from a sample of tournament_size(i), where tournament_size(1) is given by
tournament_size, tournament_size(ceiling(n_filter / per_tournament)) is given by tournament_size_last, and
tournament_size(i) for i between these values is linearly interpolated on a log scale.
FiltorSurrogateProgressive's configuration parameters are the hyperparameters of the FiltorSurrogate base class, as well as:
filter.per_tournament :: integer(1)
Number of individuals to select from each tournament. If per_tournament is not a divider of n_filter, then
the last tournament selects a random subset of n_filter %% per_tournament individuals out of the top per_tournament individuals.
Initialized to 1.
filter.tournament_size :: numeric(1)
Tournament size used for filtering. If tournament_size_last is not given, all n_filter individuals are selected
in batches of per_tournament from tournaments of this size. If it is given, then the actual tournament size is interpolated
between tournament_size and tournament_size_last on a logarithmic scale.
Tournaments with tournament size below per_tournament select per_tournament individuals without tournament, i.e. no filtering.
Initialized to 1.
filter.tournament_size_last :: numeric(1)
Tournament size used for the last tournament, see description of tournament_size. Defaults to tournament_size when not given,
i.e. all tournaments have the same size.
See FiltorSurrogate about supported operand types.
This Filtor can be created with the short access form ftr()
(ftrs() to get a list), or through the the dictionary
dict_filtors in the following way:
# preferred:
ftr("surtour", <surrogate_learner> [, <surrogate_selector>])
ftrs("surtour", <surrogate_learner> [, <surrogate_selector>]) # takes vector IDs, returns list of Filtors# long form:
dict_filtors$get("surtour", <surrogate_learner> [, <surrogate_selector>])
miesmuschel::MiesOperator -> miesmuschel::Filtor -> miesmuschel::FiltorSurrogate -> FiltorSurrogateTournament
new()Initialize the FiltorSurrogateTournament.
FiltorSurrogateTournament$new(
surrogate_learner,
surrogate_selector = SelectorBest$new()
)surrogate_learner(mlr3::LearnerRegr)
Regression learner for the surrogate model filtering algorithm.
The $surrogate_learner field will reflect this value.
surrogate_learner(mlr3::LearnerRegr)
Regression learner for the surrogate model filtering algorithm.
The $surrogate_learner field will reflect this value.
surrogate_selector(Selector)
Selector for the surrogate model filtering algorithm.
The $surrogate_selector field will reflect this value.
surrogate_selector(Selector)
Selector for the surrogate model filtering algorithm.
The $surrogate_selector field will reflect this value.
clone()The objects of this class are cloneable with this method.
FiltorSurrogateTournament$clone(deep = FALSE)deepWhether to make a deep clone.
Other filtors:
Filtor,
FiltorSurrogate,
dict_filtors_maybe,
dict_filtors_null,
dict_filtors_proxy,
dict_filtors_surprog
library("mlr3")
library("mlr3learners")
fp = ftr("surtour", lrn("regr.lm"), filter.tournament_size = 2)
p = ps(x = p_dbl(-5, 5))
known_data = data.frame(x = as.numeric(1:5))
fitnesses = as.numeric(1:5)
new_data = data.frame(x = c(2.5, 4.5))
fp$prime(p)
fp$needed_input(1)
fp$operate(new_data, known_data, fitnesses, 1)
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