bayesOpt  R Documentation 
Maximizes a user defined function within a set of bounds. After the function is sampled a predetermined number of times, a Gaussian process is fit to the results. An acquisition function is then maximized to determine the most likely location of the global maximum of the user defined function. This process is repeated for a set number of iterations.
bayesOpt( FUN, bounds, saveFile = NULL, initGrid, initPoints = 4, iters.n = 3, iters.k = 1, otherHalting = list(timeLimit = Inf, minUtility = 0), acq = "ucb", kappa = 2.576, eps = 0, parallel = FALSE, gsPoints = pmax(100, length(bounds)^3), convThresh = 1e+08, acqThresh = 1, errorHandling = "stop", plotProgress = FALSE, verbose = 1, ... )
FUN 
the function to be maximized. This function should return a
named list with at least 1 component. The first component must be named

bounds 
named list of lower and upper bounds for each 
saveFile 
character filepath (including file name and extension, .RDS) that
specifies the location to save results as they are obtained. A 
initGrid 
user specified points to sample the scoring function, should
be a 
initPoints 
Number of points to initialize the process with. Points are chosen with latin hypercube sampling within the bounds supplied. 
iters.n 
The total number of times FUN will be run after initialization. 
iters.k 
integer that specifies the number of times to sample FUN
at each Epoch (optimization step). If running in parallel, good practice
is to set 
otherHalting 
A list of other halting specifications. The process will stop if any of the following is true. These checks are only performed in between optimization steps:

acq 
acquisition function type to be used. Can be "ucb", "ei", "eips" or "poi".

kappa 
tunable parameter kappa of the upper confidence bound. Adjusts exploitation/exploration. Increasing kappa will increase the importance that uncertainty (unexplored space) has, therefore incentivising exploration. This number represents the standard deviations above 0 of your upper confidence bound. Default is 2.56, which corresponds to the ~99th percentile. 
eps 
tunable parameter epsilon of ei, eips and poi. Adjusts exploitation/exploration. This value is added to y_max after the scaling, so should between 0.1 and 0.1. Increasing eps will make the "improvement" threshold for new points higher, therefore incentivising exploitation. 
parallel 
should the process run in parallel? If TRUE, several criteria must be met:

gsPoints 
integer that specifies how many initial points to try when searching for the optimum of the acquisition function. Increase this for a higher chance to find global optimum, at the expense of more time. 
convThresh 
convergence threshold passed to 
acqThresh 
number 01. Represents the minimum percentage of the global optimal utility required for a local optimum to be included as a candidate parameter set in the next scoring function. If 1.0, only the global optimum will be used as a candidate parameter set. If 0.5, only local optimums with 50 percent of the utility of the global optimum will be used. 
errorHandling 
If FUN returns an error, how to proceed. All errors are
stored in 
plotProgress 
Should the progress of the Bayesian optimization be printed? Top graph shows the score(s) obtained at each iteration. The bottom graph shows the estimated utility of each point. This is useful to display how much utility the Gaussian Process is assuming still exists. If your utility is approaching 0, then you can be confident you are close to an optimal parameter set. 
verbose 
Whether or not to print progress to the console. If 0, nothing will be printed. If 1, progress will be printed. If 2, progress and information about new parameterscore pairs will be printed. 
... 
Other parameters passed to 
An object of class bayesOpt
containing information about the process.
FUN
The scoring function.
bounds
The bounds originally supplied.
iters
The total iterations that have been run.
initPars
The initialization parameters.
optPars
The optimization parameters.
GauProList
A list containing information on the Gaussian Processes used in optimization.
scoreSummary
A data.table
with results from the execution of FUN
at different inputs. Includes information on the epoch, iteration, function inputs, score, and any other
information returned by FUN
.
stopStatus
Information on what caused the function to stop running. Possible explenations are
time limit, minimum utility not met, errors in FUN
, iters.n was reached, or the Gaussian Process encountered
an error.
elapsedTime
The total time in seconds the function was executing.
It is highly recommended to read the GitHub for examples. There are also several vignettes available from the official CRAN Listing.
Jasper Snoek, Hugo Larochelle, Ryan P. Adams (2012) Practical Bayesian Optimization of Machine Learning Algorithms
# Example 1  Optimization of a continuous single parameter function scoringFunction < function(x) { a < exp((2x)^2)*1.5 b < exp((4x)^2)*2 c < exp((6x)^2)*1 return(list(Score = a+b+c)) } bounds < list(x = c(0,8)) Results < bayesOpt( FUN = scoringFunction , bounds = bounds , initPoints = 3 , iters.n = 2 , gsPoints = 10 ) ## Not run: # Example 2  Hyperparameter Tuning in xgboost if (requireNamespace('xgboost', quietly = TRUE)) { library("xgboost") data(agaricus.train, package = "xgboost") Folds < list( Fold1 = as.integer(seq(1,nrow(agaricus.train$data),by = 3)) , Fold2 = as.integer(seq(2,nrow(agaricus.train$data),by = 3)) , Fold3 = as.integer(seq(3,nrow(agaricus.train$data),by = 3)) ) scoringFunction < function(max_depth, min_child_weight, subsample) { dtrain < xgb.DMatrix(agaricus.train$data,label = agaricus.train$label) Pars < list( booster = "gbtree" , eta = 0.01 , max_depth = max_depth , min_child_weight = min_child_weight , subsample = subsample , objective = "binary:logistic" , eval_metric = "auc" ) xgbcv < xgb.cv( params = Pars , data = dtrain , nround = 100 , folds = Folds , prediction = TRUE , showsd = TRUE , early_stopping_rounds = 5 , maximize = TRUE , verbose = 0 ) return( list( Score = max(xgbcv$evaluation_log$test_auc_mean) , nrounds = xgbcv$best_iteration ) ) } bounds < list( max_depth = c(2L, 10L) , min_child_weight = c(1, 100) , subsample = c(0.25, 1) ) ScoreResult < bayesOpt( FUN = scoringFunction , bounds = bounds , initPoints = 3 , iters.n = 2 , iters.k = 1 , acq = "ei" , gsPoints = 10 , parallel = FALSE , verbose = 1 ) } ## End(Not run)
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