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Bayesian Optimization of Hyperparameters.
BayesianOptimization(
FUN,
bounds,
init_grid_dt = NULL,
init_points = 0,
n_iter,
acq = "ucb",
kappa = 2.576,
eps = 0,
kernel = list(type = "exponential", power = 2),
verbose = TRUE,
...
)a list of Bayesian Optimization result is returned:
Best_Par a named vector of the best hyperparameter set found
Best_Value the value of metrics achieved by the best hyperparameter set
History a data.table of the bayesian optimization history
Pred a data.table with validation/cross-validation prediction for each round of bayesian optimization history
The function to be maximized. This Function should return a named list with 2 components. The first component "Score" should be the metrics to be maximized, and the second component "Pred" should be the validation/cross-validation prediction for ensembling/stacking.
A named list of lower and upper bounds for each hyperparameter. The names of the list should be identical to the arguments of FUN. All the sample points in init_grid_dt should be in the range of bounds. Please use "L" suffix to indicate integer hyperparameter.
User specified points to sample the target function, should
be a data.frame or data.table with identical column names as bounds.
User can add one "Value" column at the end, if target function is pre-sampled.
Number of randomly chosen points to sample the target function before Bayesian Optimization fitting the Gaussian Process.
Total number of times the Bayesian Optimization is to repeated.
Acquisition function type to be used. Can be "ucb", "ei" or "poi".
ucb GP Upper Confidence Bound
ei Expected Improvement
poi Probability of Improvement
tunable parameter kappa of GP Upper Confidence Bound, to balance exploitation against exploration, increasing kappa will make the optimized hyperparameters pursuing exploration.
tunable parameter epsilon of Expected Improvement and Probability of Improvement, to balance exploitation against exploration, increasing epsilon will make the optimized hyperparameters are more spread out across the whole range.
Kernel (aka correlation function) for the underlying Gaussian Process. This parameter should be a list that specifies the type of correlation function along with the smoothness parameter. Popular choices are square exponential (default) or matern 5/2
Whether or not to print progress.
Other arguments passed on to GP_fit.
Jasper Snoek, Hugo Larochelle, Ryan P. Adams (2012) Practical Bayesian Optimization of Machine Learning Algorithms
# Example 1: Optimization
## Set Pred = 0, as placeholder
Test_Fun <- function(x) {
list(Score = exp(-(x - 2)^2) + exp(-(x - 6)^2/10) + 1/ (x^2 + 1),
Pred = 0)
}
## Set larger init_points and n_iter for better optimization result
OPT_Res <- BayesianOptimization(Test_Fun,
bounds = list(x = c(1, 3)),
init_points = 2, n_iter = 1,
acq = "ucb", kappa = 2.576, eps = 0.0,
verbose = TRUE)
if (FALSE) {
# Example 2: Parameter Tuning
library(xgboost)
data(agaricus.train, package = "xgboost")
dtrain <- xgb.DMatrix(agaricus.train$data,
label = agaricus.train$label)
cv_folds <- KFold(agaricus.train$label, nfolds = 5,
stratified = TRUE, seed = 0)
xgb_cv_bayes <- function(max_depth, min_child_weight, subsample) {
cv <- xgb.cv(params = list(booster = "gbtree", eta = 0.01,
max_depth = max_depth,
min_child_weight = min_child_weight,
subsample = subsample, colsample_bytree = 0.3,
lambda = 1, alpha = 0,
objective = "binary:logistic",
eval_metric = "auc"),
data = dtrain, nround = 100,
folds = cv_folds, prediction = TRUE, showsd = TRUE,
early_stopping_rounds = 5, maximize = TRUE, verbose = 0)
list(Score = cv$evaluation_log$test_auc_mean[cv$best_iteration],
Pred = cv$pred)
}
OPT_Res <- BayesianOptimization(xgb_cv_bayes,
bounds = list(max_depth = c(2L, 6L),
min_child_weight = c(1L, 10L),
subsample = c(0.5, 0.8)),
init_grid_dt = NULL, init_points = 10, n_iter = 20,
acq = "ucb", kappa = 2.576, eps = 0.0,
verbose = TRUE)
}
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