| dict_filtors_proxy | R Documentation |
Filtor that performs the operation in its operation configuration parameter. This can be used to make filtor operations fully parametrizable.
operation :: Filtor
Operation to perform. Must be set by the user.
This is primed when $prime() of SelectorProxy is called, and also when $operate() is called, to make changing
the operation as part of self-adaption possible. However, if the same operation gets used inside multiple SelectorProxy
objects, then it is recommended to $clone(deep = TRUE) the object before assigning them to operation to avoid
frequent re-priming.
Supported Domain classes are: p_lgl ('ParamLgl'), p_int ('ParamInt'), p_dbl ('ParamDbl'), p_fct ('ParamFct')
This Selector can be created with the short access form sel()
(sels() to get a list), or through the the dictionary
dict_selectors in the following way:
# preferred:
sel("proxy")
sels("proxy") # takes vector IDs, returns list of Selectors
# long form:
dict_selectors$get("proxy")
miesmuschel::MiesOperator -> miesmuschel::Filtor -> FiltorProxy
new()Initialize the FiltorProxy object.
FiltorProxy$new()
prime()See MiesOperator method. Primes both this operator, as well as the operator given to the operation configuration parameter.
Note that this modifies the $param_set$values$operation object.
FiltorProxy$prime(param_set)
param_set(ParamSet)
Passed to MiesOperator$prime().
invisible self.
clone()The objects of this class are cloneable with this method.
FiltorProxy$clone(deep = FALSE)
deepWhether to make a deep clone.
Other filtors:
Filtor,
FiltorSurrogate,
dict_filtors_maybe,
dict_filtors_null,
dict_filtors_surprog,
dict_filtors_surtour
Other filtor wrappers:
dict_filtors_maybe
library("mlr3")
library("mlr3learners")
fp = ftr("proxy")
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$param_set$values$operation = ftr("null")
fp$prime(p)
fp$operate(new_data, known_data, fitnesses, 1)
fp$param_set$values$operation = ftr("surprog", lrn("regr.lm"), filter.pool_factor = 2)
fp$operate(new_data, known_data, fitnesses, 1)
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