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## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,
collapse = TRUE,
comment = "#>",
message = FALSE)
## ----eval=FALSE----------------------------------------------------------
# library(xgboost)
# library(Matrix)
#
# data(agaricus.train, package = "xgboost")
# dtrain <- xgb.DMatrix(agaricus.train$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)
## ----eval=FALSE----------------------------------------------------------
# library(MlBayesOpt)
#
# res0 <- xgb_cv_opt(data = agaricus.train$data,
# label = agaricus.train$label,
# objectfun = "binary:logistic",
# evalmetric = "auc",
# n_folds = 5,
# acq = "ucb",
# init_points = 10,
# n_iter = 20)
## ----eval=FALSE----------------------------------------------------------
# # This takes a lot of time
# # fashion data is included in this package
# res0 <- xgb_cv_opt(data = fashion,
# label = y,
# objectfun = "multi:softmax",
# evalmetric = "merror",
# n_folds = 15,
# classes = 10)
## ----cran-installation, eval = FALSE-------------------------------------
# install.packages("MlBayesOpt")
## ----gh-installation, eval = FALSE---------------------------------------
# # install.packages("githubinstall")
# githubinstall::githubinstall("MlBayesOpt")
#
# # install.packages("devtools")
# devtools::install_github("ymattu/MlBayesOpt")
## ----eval=FALSE----------------------------------------------------------
# library(MlBayesOpt)
## ----eval=FALSE----------------------------------------------------------
# res0 <- svm_cv_opt(data = fashion_train,
# label = y,
# svm_kernel = "polynomial",
# degree_range = c(2L, 4L),
# n_folds = 3,
# kappa = 5,
# init_points = 4,
# n_iter = 5)
## ----eval=FALSE----------------------------------------------------------
# res0 <- rf_opt(train_data = fashion_train,
# train_label = y,
# test_data = fashion_test,
# test_label = y,
# mtry_range = c(1L, ncol(fashion_train)-1),
# num_tree = 10L,
# init_points = 4,
# n_iter = 5)
## ----eval=FALSE----------------------------------------------------------
# res0 <- xgb_cv_opt(data = fashion_train,
# label = y,
# objectfun = "multi:softmax",
# evalmetric = "merror",
# n_folds = 3,
# classes = 10,
# init_points = 4,
# n_iter = 5)
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