mlr3mbo

A new R6 and much more modular implementation for single- and multicriteria Bayesian optimization.

Design

mlr3mbo is built modular relying on the following R6 classes:

• Surrogate: Surrogate Model
• AcqFunction: Acquisition Function
• AcqOptimizer: Acquisition Function Optimizer

Based on these, Bayesian optimization loops can be written, see, e.g., bayesopt_ego for sequential single objective BO.

mlr3mbo also provides an OptimizerMbo class behaving like any other Optimizer from the bbotk package as well as a TunerMbo class behaving like any other Tuner from the mlr3tuning package.

mlr3mbo uses sensible defaults for the Surrogate, AcqFunction, AcqOptimizer, and even the loop function. See ?mbo_defaults for more details.

Simple Optimization Example

Minimize y = x^2 via sequential singlecriteria BO using a GP as surrogate and EI optimized via random search as aquisition function:

library(bbotk)
library(mlr3mbo)
library(mlr3learners)
set.seed(1)

obfun = ObjectiveRFun$new(
  fun = function(xs) list(y1 = xs$x ^ 2),
  domain = ps(x = p_dbl(lower = -5, upper = 5)),
  codomain = ps(y1 = p_dbl(tags = "minimize"))
)

terminator = trm("evals", n_evals = 10)

instance = OptimInstanceSingleCrit$new(
  objective = obfun,
  terminator = terminator
)

design = generate_design_lhs(obfun$domain, 4)$data
instance$eval_batch(design)

surrogate = SurrogateLearner$new(lrn("regr.km", control = list(trace = FALSE)))
acqfun = AcqFunctionEI$new()
acqopt = AcqOptimizer$new(
  opt("random_search", batch_size = 100),
  terminator = trm("evals", n_evals = 100)
)

optimizer = opt("mbo",
  loop_function = bayesopt_ego,
  surrogate = surrogate,
  acq_function = acqfun,
  acq_optimizer = acqopt
)
optimizer$optimize(instance)

##             x  x_domain          y1
## 1: 0.01948605 <list[1]> 0.000379706

Note that you can also use bb_optimize as a shorthand:

library(bbotk)
library(mlr3mbo)
library(mlr3learners)
set.seed(1)

fun = function(xs) list(y1 = xs$x ^ 2)

surrogate = SurrogateLearner$new(lrn("regr.km", control = list(trace = FALSE)))
acqfun = AcqFunctionEI$new()
acqopt = AcqOptimizer$new(
  opt("random_search", batch_size = 100),
  terminator = trm("evals", n_evals = 100)
)
optimizer = opt("mbo",
  loop_function = bayesopt_ego,
  surrogate = surrogate,
  acq_function = acqfun,
  acq_optimizer = acqopt
)

result = bb_optimize(
  fun,
  method = optimizer,
  lower = c(x = -5),
  upper = c(x = 5),
  max_evals = 10
)

Simple Tuning Example Using Defaults

library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(mlr3mbo)
set.seed(1)

task = tsk("pima")

learner = lrn("classif.rpart", cp = to_tune(lower = 1e-04, upper = 1e-1, logscale = TRUE))

instance = tune(
  method = "mbo",
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10
)

instance$result

##           cp learner_param_vals  x_domain classif.ce
## 1: -4.885851          <list[2]> <list[1]>  0.1914062