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A new R6 and much more modular implementation for single- and multi-objective Bayesian Optimization.
The best entry point to get familiar with mlr3mbo is provided via the
Bayesian
Optimization
chapter in the mlr3book.
mlr3mbo is built modular relying on the following
R6 classes:
Surrogate: Surrogate ModelAcqFunction: Acquisition FunctionAcqOptimizer: Acquisition Function Optimizer
Based on these, Bayesian Optimization (BO) 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.
Minimize the two-dimensional 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)## x1 x2 x_domain y
## <num> <num> <list> <num>
## 1: 3.090821 2.299709 <list[2]> 0.4104925
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.
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 = 1, logscale = TRUE))
instance = tune(
tuner = tnr("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
## <num> <list> <list> <num>
## 1: -6.188733 <list[2]> <list[1]> 0.2382812