optimize.bayesian.all: Bayesian Optimization, All Results Set

In Laurae2/Laurae2: Laurae's Simplified R

Description

optimize.bayesian.all gets all the results issued from optimize.bayesian.

Usage

optimize.bayesian.all(optimized)

Arguments

optimized

Type: List returned from optimize.bayesian. The object to look for all the results.

Details

Please see vignette for demos: vignette("optimize.bayesian", package = "Laurae2") or help_me("optimize.bayesian").

Value

A data.frame containing all the results set from the bayesian optimization. A column y refers to the loss function value for the according parameters on the same row.

Examples

## Not run: 
library(xgboost)
library(mlrMBO)

# Load demo data
data(EuStockMarkets)

# Transform dataset to "quantiles"
for (i in 1:4) {
  EuStockMarkets[, i] <- (ecdf(EuStockMarkets[, i]))(EuStockMarkets[, i])
}

# Create datasets: 1500 observations for training, 360 for testing
# Features are:
# -- Deutscher Aktienindex (DAX),
# -- Swiss Market Index (SMI),
# -- and Cotation Assistee en Continu (CAC)
# Label is Financial Times Stock Exchange 100 Index (FTSE)
dtrain <- xgb.DMatrix(EuStockMarkets[1:1500, 1:3], label = EuStockMarkets[1:1500, 4])
dval <- xgb.DMatrix(EuStockMarkets[1501:1860, 1:3], label = EuStockMarkets[1501:1860, 4])

# Create watchlist for monitoring metric
watchlist <- list(train = dtrain, eval = dval)

# Our loss function to optimize: minimize RMSE
xgboost_optimization <- function(x) {

  # Train the model
  gc(verbose = FALSE)
  set.seed(1)
  model <- xgb.train(params = list(max_depth = x[1],
                                   subsample = x[2],
                                   tree_method = x[3],
                                   eta = 0.2,
                                   nthread = 1,
                                   objective = "reg:linear",
                                   eval_metric = "rmse"),
                     data = dtrain, # Warn: Access using parent environment
                     nrounds = 9999999,
                     watchlist = watchlist, # Warn: Access using parent environment
                     early_stopping_rounds = 5,
                     verbose = 0)
  score <- model$best_score
  rm(model)
  return(score)

}

# The paramters: max_depth in [1, 15], subsample in [0.1, 1], and tree_method IN {exact, hist}
my_parameters <- makeParamSet(
  makeIntegerParam(id = "max_depth", lower = 1, upper = 15),
  makeNumericParam(id = "subsample", lower = 0.1, upper = 1),
  makeDiscreteParam(id = "tree_method", values = c("exact", "hist"))
)

# Perform optimization
optimization <- optimize.bayesian(loss_func = xgboost_optimization,
                                  param_set = my_parameters,
                                  seed = 1,
                                  maximize = FALSE,
                                  initialization = 10,
                                  max_evaluations = 25,
                                  time_budget = 30,
                                  verbose = TRUE)

# What was optimized? And their loss?
optimize.bayesian.all(optimization)

## End(Not run)

Laurae2/Laurae2 documentation built on May 18, 2019, 11:27 p.m.