dict_filtors_surtour: Tournament Surrogate Model Filtering

In miesmuschel: Mixed Integer Evolution Strategies

Tournament Surrogate Model Filtering

Description

Performs tournament surrogate model filtering. A surrogate model is used, as described in the parent class FiltorSurrogate.

Algorithm

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.

Configuration Parameters

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.

Supported Operand Types

See FiltorSurrogate about supported operand types.

Dictionary

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>])

Super classes

miesmuschel::MiesOperator -> miesmuschel::Filtor -> miesmuschel::FiltorSurrogate -> FiltorSurrogateTournament

Methods

Public methods

• FiltorSurrogateTournament$new()

• FiltorSurrogateTournament$clone()

Inherited methods

• miesmuschel::MiesOperator$help()
• miesmuschel::MiesOperator$operate()
• miesmuschel::MiesOperator$print()
• miesmuschel::MiesOperator$repr()
• miesmuschel::Filtor$needed_input()
• miesmuschel::FiltorSurrogate$prime()

---

Method new()

Initialize the FiltorSurrogateTournament.

Usage

FiltorSurrogateTournament$new(
  surrogate_learner,
  surrogate_selector = SelectorBest$new()
)

Arguments

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.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FiltorSurrogateTournament$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other filtors: Filtor, FiltorSurrogate, dict_filtors_maybe, dict_filtors_null, dict_filtors_proxy, dict_filtors_surprog

Examples

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 = 1:5)
fitnesses = 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)

miesmuschel documentation built on Sept. 11, 2024, 8:23 p.m.