xgb_cv_opt: Bayesian Optimization for XGboost(Cross Validation)

In MlBayesOpt: Hyper Parameter Tuning for Machine Learning, Using Bayesian Optimization

Description

Bayesian Optimization for XGboost (Cross Validation)

Usage

xgb_cv_opt(data, label, objectfun, evalmetric, n_folds, eta_range = c(0.1,
  1L), max_depth_range = c(4L, 6L), nrounds_range = c(70, 160L),
  subsample_range = c(0.1, 1L), bytree_range = c(0.4, 1L),
  init_points = 4, n_iter = 10, acq = "ei", kappa = 2.576, eps = 0,
  optkernel = list(type = "exponential", power = 2), classes = NULL,
  seed = 0)

Arguments

data	data
label	label for classification
objectfun	Specify the learning task and the corresponding learning objective reg:linear linear regression (Default). reg:logistic logistic regression. binary:logistic logistic regression for binary classification. Output probability. binary:logitraw logistic regression for binary classification, output score before logistic transformation. multi:softmax set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to num_class - 1. multi:softprob same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class. rank:pairwise set xgboost to do ranking task by minimizing the pairwise loss.
evalmetric	evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). error binary classification error rate rmse Rooted mean square error logloss negative log-likelihood function auc Area under curve merror Exact matching error, used to evaluate multi-class classification
n_folds	K for cross Validation
eta_range	The range of eta(default is c(0.1, 1L))
max_depth_range	The range of max_depth(default is c(4L, 6L))
nrounds_range	The range of nrounds(default is c(70, 160L))
subsample_range	The range of subsample rate(default is c(0.1, 1L))
bytree_range	The range of colsample_bytree rate(default is c(0.4, 1L)
init_points	Number of randomly chosen points to sample the target function before Bayesian Optimization fitting the Gaussian Process.
n_iter	Total number of times the Bayesian Optimization is to repeated.
acq	Acquisition function type to be used. Can be "ucb", "ei" or "poi". #' ucb GP Upper Confidence Bound ei Expected Improvement poi Probability of Improvement
kappa	kappa tunable parameter kappa of GP Upper Confidence Bound, to balance exploitation against exploration, increasing kappa will make the optimized hyperparameters pursuing exploration.
eps	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.
optkernel	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
classes	set the number of classes. To use only with multiclass objectives.
seed	set seed.(default is 0)

Value

The score you specified in the evalmetric option and 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

Examples

library(MlBayesOpt)

set.seed(71)
res0 <- xgb_cv_opt(data = iris,
                   label = Species,
                   objectfun = "multi:softmax",
                   evalmetric = "mlogloss",
                   n_folds = 3,
                   classes = 3,
                   init_points = 2,
                   n_iter = 1)

MlBayesOpt documentation built on May 2, 2019, 9:20 a.m.