Set the optimization method and control parameters for tuning of model parameters.
set_optim_bayes(object, ...)# S3 method for ModelSpecification
set_optim_bayes(
object,
num_init = 5,
times = 10,
each = 1,
acquisition = c("ucb", "ei", "eips", "poi"),
kappa = stats::qnorm(conf),
conf = 0.995,
epsilon = 0,
control = list(),
packages = c("ParBayesianOptimization", "rBayesianOptimization"),
random = FALSE,
progress = verbose,
verbose = 0,
...
)
set_optim_bfgs(object, ...)
# S3 method for ModelSpecification
set_optim_bfgs(
object,
times = 10,
control = list(),
random = FALSE,
progress = FALSE,
verbose = 0,
...
)
set_optim_grid(object, ...)
# S3 method for TrainingParams
set_optim_grid(object, random = FALSE, progress = FALSE, ...)
# S3 method for ModelSpecification
set_optim_grid(object, ...)
# S3 method for TunedInput
set_optim_grid(object, ...)
# S3 method for TunedModel
set_optim_grid(object, ...)
set_optim_pso(object, ...)
# S3 method for ModelSpecification
set_optim_pso(
object,
times = 10,
each = NULL,
control = list(),
random = FALSE,
progress = FALSE,
verbose = 0,
...
)
set_optim_sann(object, ...)
# S3 method for ModelSpecification
set_optim_sann(
object,
times = 10,
control = list(),
random = FALSE,
progress = FALSE,
verbose = 0,
...
)
set_optim_method(object, ...)
# S3 method for ModelSpecification
set_optim_method(
object,
fun,
label = "Optimization Function",
packages = character(),
params = list(),
random = FALSE,
progress = FALSE,
verbose = FALSE,
...
)
Argument object updated with the specified optimization method
and control parameters.
input or model object.
arguments passed to the TrainingParams method of
set_optim_grid from its other methods.
number of grid points to sample for the initialization of Bayesian optimization.
maximum number of times to repeat the optimization step. Multiple sets of model parameters are evaluated automatically at each step of the BFGS algorithm to compute a finite-difference approximation to the gradient.
number of times to sample and evaluate model parameters at each
optimization step. This is the swarm size in particle swarm optimization,
which defaults to floor(10 + 2 * sqrt(length(bounds))).
character string specifying the acquisition function as
"ucb" (upper confidence bound), "ei" (expected improvement),
"eips" (expected improvement per second), or "poi"
(probability of improvement).
upper confidence bound ("ucb") quantile or its
probability to balance exploitation against exploration. Argument
kappa takes precedence if both are given and multiplies the
predictive standard deviation added to the predictive mean in the
acquisition function. Larger values encourage exploration of the model
parameter space.
improvement methods ("ei", "eips", and
"poi") parameter to balance exploitation against exploration.
Values should be between -0.1 and 0.1 with larger ones encouraging
exploration.
list of control parameters passed to
bayesOpt by set_optim_bayes
with package "ParBayesianOptimization", to
BayesianOptimization by
set_optim_bayes with package "rBayesianOptimization", to
optim by set_optim_bfgs and
set_optim_sann, and to psoptim by
set_optim_pso.
R package or packages to use for the optimization method, or
an empty vector if none are needed. The first package in
set_optim_bayes is used unless otherwise specified by the user.
number of points to sample for a random grid search, or
FALSE for an exhaustive grid search. Used when a grid search is
specified or as the fallback method for non-numeric model parameters
present during other optimization methods.
logical indicating whether to display iterative progress during optimization.
numeric or logical value specifying the level of progress
detail to print, with 0 (FALSE) indicating none and 1 (TRUE)
or higher indicating increasing amounts of detail.
user-defined optimization function to which the arguments below
are passed in order. An ellipsis can be included in the function
definition when using only a subset of the arguments and ignoring others.
A tibble returned by the function with the same number of rows as model
evaluations will be included in a TrainingStep summary of
optimization results; other types of return values will be ignored.
function that takes a numeric vector or list of named
model parameters as the first argument, optionally accepts the maximum
number of iterations as argument max_iter, and returns a scalar
measure of performance to be maximized. Parameter names are available
from the grid and bounds arguments described below. If
the function cannot be evaluated at a given set of parameter values,
then -Inf is returned.
data frame containing a tuning grid of all model parameters.
named list of lower and upper bounds for each finite
numeric model parameter in grid. The types (integer or double)
of the original parameter values are preserved in the bounds.
list of optimization parameters as supplied to
set_optim_method.
list of the progress and verbose values.
character descriptor for the optimization method.
list of user-specified model parameters to be passed to
fun.
The optimization functions implement the following methods.
set_optim_bayesBayesian optimization with a Gaussian process model (Snoek et al. 2012).
set_optim_bfgslimited-memory modification of quasi-Newton BFGS optimization (Byrd et al. 1995).
set_optim_gridexhaustive or random grid search.
set_optim_psoparticle swarm optimization (Bratton and Kennedy 2007, Zambrano-Bigiarini et al. 2013).
set_optim_sannsimulated annealing (Belisle 1992). This method depends critically on the control parameter settings. It is not a general-purpose method but can be very useful in getting to good parameter values on a very rough optimization surface.
set_optim_methoduser-defined optimization function.
The package-defined optimization functions evaluate and return values of the
tuning parameters that are of same type (e.g. integer, double, character) as
given in the object grid. Sequential optimization of numeric tuning
parameters is performed over a hypercube defined by their minimum and maximum
grid values. Non-numeric parameters are optimized with grid searches.
Belisle, C. J. P. (1992). Convergence theorems for a class of simulated annealing algorithms on Rd. Journal of Applied Probability, 29, 885–895.
Bratton, D. & Kennedy, J. (2007), Defining a standard for particle swarm optimization. In IEEE Swarm Intelligence Symposium, 2007 (pp. 120-127).
Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16, 1190–1208.
Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. arXiv:1206.2944 [stat.ML].
Zambrano-Bigiarini, M., Clerc, M., & Rojas, R. (2013). Standard particle swarm optimisation 2011 at CEC-2013: A baseline for future PSO improvements. In IEEE Congress on Evolutionary Computation, 2013 (pp. 2337-2344).
BayesianOptimization,
bayesOpt, optim,
psoptim, set_monitor,
set_predict, set_strata
ModelSpecification(
sale_amount ~ ., data = ICHomes,
model = TunedModel(GBMModel)
) %>% set_optim_bayes
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