mlr3mbo 0.2.7
- refactor: refactored
SurrogateLearner
and
SurrogateLearnerCollection
to allow updating on an
asynchronous Archive
.
- feat: added experimental
OptimizerAsyncMbo
,
OptimizerADBO
, TunerAsyncMbo
, and
TunerADBO
that allow for asynchronous optimization.
- feat: added
AcqFunctionStochasticCB
and
AcqFunctionStochasticEI
that are useful for asynchronous
optimization.
- doc: minor changes to highlight differences between batch and
asynchronous objects related to asynchronous support.
- refactor:
AcqFunction
s and AcqOptimizer
gained a reset()
method.
mlr3mbo 0.2.6
- refactor: Extract internal tuned values in instance.
mlr3mbo 0.2.5
- docs: Move vignette to mlr3book.
- feat: Add
AcqFunctionMulti
that can wrap multiple
acquisition functions resulting in a multi-objective acquisition
function problem.
- feat: Support callbacks in
AcqOptimizer
.
- feat: Allow
AcqFunctionEI
to be adjusted by epsilon to
strengthen exploration.
mlr3mbo 0.2.4
- fix: Improve runtime of
AcqOptimizer
by setting
check_values = FALSE
.
mlr3mbo 0.2.3
- compatibility: Work with new bbotk and mlr3tuning version
1.0.0.
mlr3mbo 0.2.2
- refactor: compatibility with upcoming paradox upgrade.
- feat:
OptimizerMbo
and TunerMbo
now update
the Surrogate
a final time after the optimization process
finished to ensure that the Surrogate
correctly reflects
the state of being trained on all data seen during optimization.
- fix:
AcqFunction
domain construction now respects
Surrogate
cols_x field.
- feat: support more than one candidate point as a result of
acquisition function optimization even for non-batch acquisition
functions.
- feat: added
default_gp
and default_rf
helpers that allow for construction of a default Gaussian Process and
random forest as for example used within
default_surrogate
.
- refactor: changed Gaussian Process and random forest defaults (in
default_gp
and default_rf
and therefore also
in default_surrogate
). Gaussian Process now uses a
"matern5_2"
kernel. Random forest now uses 100 trees. The
number of trees used in the fallback random forest was reduced to
10.
mlr3mbo 0.2.1
- docs: updated some references in vignette.
- refactor: minor clean up of the internal structure of all loop
functions.
- perf: default initial design constructed based on a Sobol sequence
in all loop functions.
- refactor: no longer depend on
mlr3tuning
but import
instead.
- refactor:
srlrn
sugar function now can construct both a
SurrogateLearner
and
SurrogateLearnerCollection
; dropped
srlrnc
.
- feat: added
AcqFunctionSD
, AcqFunctionEHVI
and AcqFunctionEHVIGH
, introduced bayesopt_emo
loop function.
- feat:
AcqFunction
s now include a $packages
field stating required packages which are checked for whether their
namespace can be loaded prior to optimization.
- fix: fixed bug in
fix_xdt_missing()
helper
function.
- BREAKING CHANGE: renaming
default_loopfun
->
default_loop_function
, default_acqfun
->
default_acqfunction
, default_acqopt
->
default_acqoptimizer
.
- BREAKING CHANGE:
result_function
s now replaced by
ResultAssigner
s.
- BREAKING CHANGE: renamed
$model
field of all
Surrogate
classes to $learner
.
- BREAKING CHANGE: For all
Surrogate
and
AcquisitionFunction
classes fields *_cols
renamed to cols_*
(e.g., x_cols
to
cols_x
).
mlr3mbo 0.1.2
- refactor: adapt to mlr3tuning 0.18.0.
- feat: Acquisition functions now assert whether surrogates match
their required predict type.
- fix: Unloading
mlr3mbo
removes optimizers and tuners
from the dictionaries.
- docs: faster examples.
- feat: characters in surrogate regression tasks are no longer
automatically converted to factors.
default_surrogate
now
respects this and gained an appropriate pipeline step.
- feat:
AcqFunctionAEI
added.
- docs: fix of docs, README and bibentries.
mlr3mbo 0.1.1