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feat: add asynchronous decentralized bayesian optimization (#145)
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| #' @title Acquisition Function Stochastic Confidence Bound | ||
| #' | ||
| #' @include AcqFunction.R | ||
| #' @name mlr_acqfunctions_stochastic_cb | ||
| #' | ||
| #' @templateVar id stochastic_cb | ||
| #' @template section_dictionary_acqfunctions | ||
| #' | ||
| #' @description | ||
| #' Lower / Upper Confidence Bound with lambda sampling and decay. | ||
| #' The initial \eqn{\lambda} is drawn from an uniform distribution between `min_lambda` and `max_lambda` or from an exponential distribution with rate `1 / lambda`. | ||
| #' \eqn{\lambda} is updated after each update by the formula `lambda * exp(-rate * (t %% period))`, where `t` is the number of times the acquisition function has been updated. | ||
| #' | ||
| #' While this acquisition function usually would be used within an asynchronous optimizer, e.g., [OptimizerAsyncMbo], | ||
| #' it can in principle also be used in synchronous optimizers, e.g., [OptimizerMbo]. | ||
| #' | ||
| #' @section Parameters: | ||
| #' * `"lambda"` (`numeric(1)`)\cr | ||
| #' \eqn{\lambda} value for sampling from the exponential distribution. | ||
| #' Defaults to `1.96`. | ||
| #' * `"min_lambda"` (`numeric(1)`)\cr | ||
| #' Minimum value of \eqn{\lambda}for sampling from the uniform distribution. | ||
| #' Defaults to `0.01`. | ||
| #' * `"max_lambda"` (`numeric(1)`)\cr | ||
| #' Maximum value of \eqn{\lambda} for sampling from the uniform distribution. | ||
| #' Defaults to `10`. | ||
| #' * `"distribution"` (`character(1)`)\cr | ||
| #' Distribution to sample \eqn{\lambda} from. | ||
| #' One of `c("uniform", "exponential")`. | ||
| #' Defaults to `uniform`. | ||
| #' * `"rate"` (`numeric(1)`)\cr | ||
| #' Rate of the exponential decay. | ||
| #' Defaults to `0` i.e. no decay. | ||
| #' * `"period"` (`integer(1)`)\cr | ||
| #' Period of the exponential decay. | ||
| #' Defaults to `NULL`, i.e., the decay has no period. | ||
| #' | ||
| #' @section Note: | ||
| #' * This acquisition function always also returns its current (`acq_lambda`) and original (`acq_lambda_0`) \eqn{\lambda}. | ||
| #' These values will be logged into the [bbotk::ArchiveBatch] of the [bbotk::OptimInstanceBatch] of the [AcqOptimizer] and | ||
| #' therefore also in the [bbotk::Archive] of the actual [bbotk::OptimInstance] that is to be optimized. | ||
| #' | ||
| #' @references | ||
| #' * `r format_bib("snoek_2012")` | ||
| #' * `r format_bib("egele_2023")` | ||
| #' | ||
| #' @family Acquisition Function | ||
| #' @export | ||
| #' @examples | ||
| #' if (requireNamespace("mlr3learners") & | ||
| #' requireNamespace("DiceKriging") & | ||
| #' requireNamespace("rgenoud")) { | ||
| #' library(bbotk) | ||
| #' library(paradox) | ||
| #' library(mlr3learners) | ||
| #' library(data.table) | ||
| #' | ||
| #' fun = function(xs) { | ||
| #' list(y = xs$x ^ 2) | ||
| #' } | ||
| #' domain = ps(x = p_dbl(lower = -10, upper = 10)) | ||
| #' codomain = ps(y = p_dbl(tags = "minimize")) | ||
| #' objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) | ||
| #' | ||
| #' instance = OptimInstanceBatchSingleCrit$new( | ||
| #' objective = objective, | ||
| #' terminator = trm("evals", n_evals = 5)) | ||
| #' | ||
| #' instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) | ||
| #' | ||
| #' learner = default_gp() | ||
| #' | ||
| #' surrogate = srlrn(learner, archive = instance$archive) | ||
| #' | ||
| #' acq_function = acqf("stochastic_cb", surrogate = surrogate, lambda = 3) | ||
| #' | ||
| #' acq_function$surrogate$update() | ||
| #' acq_function$update() | ||
| #' acq_function$eval_dt(data.table(x = c(-1, 0, 1))) | ||
| #' } | ||
| AcqFunctionStochasticCB = R6Class("AcqFunctionStochasticCB", | ||
| inherit = AcqFunction, | ||
|
|
||
| public = list( | ||
|
|
||
| #' @description | ||
| #' Creates a new instance of this [R6][R6::R6Class] class. | ||
| #' | ||
| #' @param surrogate (`NULL` | [SurrogateLearner]). | ||
| #' @param lambda (`numeric(1)`). | ||
| #' @param min_lambda (`numeric(1)`). | ||
| #' @param max_lambda (`numeric(1)`). | ||
| #' @param distribution (`character(1)`). | ||
| #' @param rate (`numeric(1)`). | ||
| #' @param period (`NULL` | `integer(1)`). | ||
| initialize = function( | ||
| surrogate = NULL, | ||
| lambda = 1.96, | ||
| min_lambda = 0.01, | ||
| max_lambda = 10, | ||
| distribution = "uniform", | ||
| rate = 0, | ||
| period = NULL | ||
| ) { | ||
| assert_r6(surrogate, "SurrogateLearner", null.ok = TRUE) | ||
| private$.lambda = assert_number(lambda, lower = .Machine$double.neg.eps, null.ok = TRUE) | ||
| private$.min_lambda = assert_number(min_lambda, lower = .Machine$double.neg.eps, null.ok = TRUE) | ||
| private$.max_lambda = assert_number(max_lambda, lower = .Machine$double.neg.eps, null.ok = TRUE) | ||
| private$.distribution = assert_choice(distribution, choices = c("uniform", "exponential")) | ||
|
|
||
| if (private$.distribution == "uniform" && (is.null(private$.min_lambda) || is.null(private$.max_lambda))) { | ||
| stop('If `distribution` is "uniform", `min_lambda` and `max_lambda` must be set.') | ||
| } | ||
|
|
||
| if (private$.distribution == "exponential" && is.null(private$.lambda)) { | ||
| stop('If `distribution` is "exponential", `lambda` must be set.') | ||
| } | ||
|
|
||
| private$.rate = assert_number(rate, lower = 0) | ||
| private$.period = assert_int(period, lower = 1, null.ok = TRUE) | ||
|
|
||
| constants = ps(lambda = p_dbl(lower = 0)) | ||
|
|
||
| super$initialize("acq_cb", | ||
| constants = constants, | ||
| surrogate = surrogate, | ||
| requires_predict_type_se = TRUE, | ||
| direction = "same", | ||
| label = "Stochastic Lower / Upper Confidence Bound", | ||
| man = "mlr3mbo::mlr_acqfunctions_stochastic_cb") | ||
| }, | ||
|
|
||
| #' @description | ||
| #' Update the acquisition function. | ||
| #' Samples and decays lambda. | ||
| update = function() { | ||
| # sample lambda | ||
| if (is.null(self$constants$values$lambda)) { | ||
|
|
||
| if (private$.distribution == "uniform") { | ||
| lambda = runif(1, private$.min_lambda, private$.max_lambda) | ||
| } else { | ||
| lambda = rexp(1, 1 / private$.lambda) | ||
| } | ||
|
|
||
| private$.lambda_0 = lambda | ||
| self$constants$values$lambda = lambda | ||
| } | ||
|
|
||
| # decay lambda | ||
| if (private$.rate > 0) { | ||
| lambda_0 = private$.lambda_0 | ||
| period = private$.period | ||
| t = if (is.null(period)) private$.t else private$.t %% period | ||
| rate = private$.rate | ||
|
|
||
| self$constants$values$lambda = lambda_0 * exp(-rate * t) | ||
| private$.t = t + 1L | ||
| } | ||
| }, | ||
|
|
||
| #' @description | ||
| #' Reset the acquisition function. | ||
| #' Resets the private update counter `.t` used within the epsilon decay. | ||
| reset = function() { | ||
| private$.t = 0L | ||
| } | ||
| ), | ||
|
|
||
| private = list( | ||
| .lambda = NULL, | ||
| .min_lambda = NULL, | ||
| .max_lambda = NULL, | ||
| .distribution = NULL, | ||
| .rate = NULL, | ||
| .period = NULL, | ||
| .lambda_0 = NULL, | ||
| .t = 0L, | ||
| .fun = function(xdt, lambda) { | ||
| p = self$surrogate$predict(xdt) | ||
| cb = p$mean - self$surrogate_max_to_min * lambda * p$se | ||
| data.table(acq_cb = cb, acq_lambda = lambda, acq_lambda_0 = private$.lambda_0) | ||
| } | ||
| ) | ||
| ) | ||
|
|
||
| mlr_acqfunctions$add("stochastic_cb", AcqFunctionStochasticCB) | ||
|
|
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