Package website: release  dev
A new R6 and much more modular implementation for single and multiobjective Bayesian Optimization.
Get Started
The best entry point to get familiar with mlr3mbo
is provided via the Bayesian Optimization chapter in the mlr3book
.
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 (BO) loops can be written, see, e.g., bayesopt_ego
for sequential singleobjective 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 the twodimensional Branin function via sequential BO using a GP as surrogate and EI as acquisition function optimized via a local serch:
library(bbotk)
library(mlr3mbo)
library(mlr3learners)
set.seed(1)
fun = function(xdt) {
y = branin(xdt[["x1"]], xdt[["x2"]])
data.table(y = y)
}
domain = ps(
x1 = p_dbl(5, 10),
x2 = p_dbl(0, 15)
)
codomain = ps(
y = p_dbl(tags = "minimize")
)
objective = ObjectiveRFunDt$new(
fun = fun,
domain = domain,
codomain = codomain
)
instance = oi(
objective = objective,
terminator = trm("evals", n_evals = 25)
)
surrogate = srlrn(lrn("regr.km", control = list(trace = FALSE)))
acq_function = acqf("ei")
acq_optimizer = acqo(
opt("local_search", n_initial_points = 10, initial_random_sample_size = 1000, neighbors_per_point = 10),
terminator = trm("evals", n_evals = 3000)
)
optimizer = opt("mbo",
loop_function = bayesopt_ego,
surrogate = surrogate,
acq_function = acq_function,
acq_optimizer = acq_optimizer
)
optimizer$optimize(instance)
We can quickly visualize the contours of the objective function (on log scale) as well as the sampling behavior of our BO run (lighter blue colours indicating points that were evaluated in later stages of the optimization process; the first batch is given by the initial design).
library(ggplot2)
grid = generate_design_grid(instance$search_space, resolution = 1000L)$data
grid[, y := branin(x1 = x1, x2 = x2)]
ggplot(aes(x = x1, y = x2, z = log(y)), data = grid) +
geom_contour(colour = "black") +
geom_point(aes(x = x1, y = x2, colour = batch_nr), data = instance$archive$data) +
labs(x = expression(x[1]), y = expression(x[2])) +
theme_minimal() +
theme(legend.position = "bottom")
Note that you can also use bb_optimize
as a shorthand instead of constructing an optimization instance.
Simple Tuning Example
library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(mlr3mbo)
set.seed(1)
task = tsk("pima")
learner = lrn("classif.rpart", cp = to_tune(lower = 1e04, upper = 1, logscale = TRUE))
instance = tune(
tuner = tnr("mbo"),
task = task,
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
term_evals = 10)
instance$result