The goal of Vizard is to ...
You can install the released version of Vizard from CRAN with:
remotes::install_github("illoRocks/Vizard")
This is a basic example which shows you how to solve a common problem:
library(xgboost)
library(tweakr)
library(purrr)
library(Vizard)
# load data ---------------------------------------------------------------
data(agaricus.train, package = "xgboost")
data(agaricus.test, package = "xgboost")
# choose params -----------------------------------------------------------
params <- paramize(
list(
eta.dbl = c(.01, .4),
max_depth.int = c(1, 3),
silent = 1, nthread = 2,
objective = "binary:logistic", eval_metric = "auc"
),
search_len = 10,
search_method = "random"
)
params <- pmap(params, list)
# train-function ----------------------------------------------------------
train_xgb <- function(param, dtrain, dtest, ...) {
watchlist <- list(train = dtrain, eval = dtest)
xgb.train(param, dtrain, nrounds = 3, watchlist, ...)
}
# scores per param set ----------------------------------------------------
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
models <- map(params, train_xgb, dtrain = dtrain, dtest = dtest, early_stopping_rounds = 20)
auc_scores <- map_dbl(models, "best_score")
# visulize scores ---------------------------------------------------------
parameter <- parse_arguments(params, auc_scores)
run_app(parameters = parameter)
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