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gbt.R
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gbt.R
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#' Gradient Boosted Trees using XGBoost
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/gbt.html} for an example in Radiant
#'
#' @param dataset Dataset
#' @param rvar The response variable in the model
#' @param evar Explanatory variables in the model
#' @param type Model type (i.e., "classification" or "regression")
#' @param lev Level to use as the first column in prediction output
#' @param max_depth Maximum 'depth' of tree
#' @param learning_rate Learning rate (eta)
#' @param min_split_loss Minimal improvement (gamma)
#' @param nrounds Number of trees to create
#' @param min_child_weight Minimum number of instances allowed in each node
#' @param subsample Subsample ratio of the training instances (0-1)
#' @param early_stopping_rounds Early stopping rule
#' @param nthread Number of parallel threads to use. Defaults to 12 if available
#' @param wts Weights to use in estimation
#' @param seed Random seed to use as the starting point
#' @param data_filter Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
#' @param arr Expression to arrange (sort) the data on (e.g., "color, desc(price)")
#' @param rows Rows to select from the specified dataset
#' @param envir Environment to extract data from
#' @param ... Further arguments to pass to xgboost
#'
#' @return A list with all variables defined in gbt as an object of class gbt
#'
#' @examples
#' \dontrun{
#' gbt(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
#' gbt(titanic, "survived", c("pclass", "sex")) %>% str()
#' }
#' gbt(
#' titanic, "survived", c("pclass", "sex"), lev = "Yes",
#' early_stopping_rounds = 0, nthread = 1
#' ) %>% summary()
#' gbt(
#' titanic, "survived", c("pclass", "sex"),
#' early_stopping_rounds = 0, nthread = 1
#' ) %>% str()
#' gbt(
#' titanic, "survived", c("pclass", "sex"),
#' eval_metric = paste0("error@", 0.5 / 6), nthread = 1
#' ) %>% str()
#' gbt(
#' diamonds, "price", c("carat", "clarity"), type = "regression", nthread = 1
#' ) %>% summary()
#'
#' @seealso \code{\link{summary.gbt}} to summarize results
#' @seealso \code{\link{plot.gbt}} to plot results
#' @seealso \code{\link{predict.gbt}} for prediction
#'
#' @importFrom xgboost xgboost xgb.importance
#' @importFrom lubridate is.Date
#'
#' @export
gbt <- function(dataset, rvar, evar, type = "classification", lev = "",
max_depth = 6, learning_rate = 0.3, min_split_loss = 0,
min_child_weight = 1, subsample = 1,
nrounds = 100, early_stopping_rounds = 10,
nthread = 12, wts = "None", seed = NA,
data_filter = "", arr = "", rows = NULL,
envir = parent.frame(), ...) {
if (rvar %in% evar) {
return("Response variable contained in the set of explanatory variables.\nPlease update model specification." %>%
add_class("gbt"))
}
vars <- c(rvar, evar)
if (is.empty(wts, "None")) {
wts <- NULL
} else if (is_string(wts)) {
wtsname <- wts
vars <- c(rvar, evar, wtsname)
}
df_name <- if (is_string(dataset)) dataset else deparse(substitute(dataset))
dataset <- get_data(dataset, vars, filt = data_filter, arr = arr, rows = rows, envir = envir) %>%
mutate_if(is.Date, as.numeric)
nr_obs <- nrow(dataset)
if (!is.empty(wts, "None")) {
if (exists("wtsname")) {
wts <- dataset[[wtsname]]
dataset <- select_at(dataset, .vars = base::setdiff(colnames(dataset), wtsname))
}
if (length(wts) != nrow(dataset)) {
return(
paste0("Length of the weights variable is not equal to the number of rows in the dataset (", format_nr(length(wts), dec = 0), " vs ", format_nr(nrow(dataset), dec = 0), ")") %>%
add_class("gbt")
)
}
}
not_vary <- colnames(dataset)[summarise_all(dataset, does_vary) == FALSE]
if (length(not_vary) > 0) {
return(paste0("The following variable(s) show no variation. Please select other variables.\n\n** ", paste0(not_vary, collapse = ", "), " **") %>%
add_class("gbt"))
}
rv <- dataset[[rvar]]
if (type == "classification") {
if (lev == "") {
if (is.factor(rv)) {
lev <- levels(rv)[1]
} else {
lev <- as.character(rv) %>%
as.factor() %>%
levels() %>%
.[1]
}
}
if (lev != levels(rv)[1]) {
dataset[[rvar]] <- relevel(dataset[[rvar]], lev)
}
}
vars <- evar
## in case : is used
if (length(vars) < (ncol(dataset) - 1)) {
vars <- evar <- colnames(dataset)[-1]
}
gbt_input <- list(
max_depth = max_depth,
learning_rate = learning_rate,
min_split_loss = min_split_loss,
nrounds = nrounds,
min_child_weight = min_child_weight,
subsample = subsample,
early_stopping_rounds = early_stopping_rounds,
nthread = nthread
)
## checking for extra args
extra_args <- list(...)
extra_args_names <- names(extra_args)
check_args <- function(arg, default, inp = gbt_input) {
if (!arg %in% extra_args_names) inp[[arg]] <- default
inp
}
if (type == "classification") {
gbt_input <- check_args("objective", "binary:logistic")
gbt_input <- check_args("eval_metric", "auc")
dty <- as.integer(dataset[[rvar]] == lev)
} else {
gbt_input <- check_args("objective", "reg:squarederror")
gbt_input <- check_args("eval_metric", "rmse")
dty <- dataset[[rvar]]
}
## adding data
dtx <- onehot(dataset[, -1, drop = FALSE])[, -1, drop = FALSE]
gbt_input <- c(gbt_input, list(data = dtx, label = dty), ...)
## based on https://stackoverflow.com/questions/14324096/setting-seed-locally-not-globally-in-r/14324316#14324316
seed <- gsub("[^0-9]", "", seed)
if (!is.empty(seed)) {
if (exists(".Random.seed")) {
gseed <- .Random.seed
on.exit(.Random.seed <<- gseed)
}
set.seed(seed)
}
## capturing the iteration history
output <- capture.output(model <<- do.call(xgboost::xgboost, gbt_input))
## adding residuals for regression models
if (type == "regression") {
model$residuals <- dataset[[rvar]] - predict(model, dtx)
} else {
model$residuals <- NULL
}
## adding feature importance information
## replaced by premutation importance
# model$importance <- xgboost::xgb.importance(model = model)
## gbt model object does not include the data by default
model$model <- dataset
rm(dataset, dty, dtx, rv, envir) ## dataset not needed elsewhere
gbt_input$data <- gbt_input$label <- NULL
## needed to work with prediction functions
check <- ""
as.list(environment()) %>% add_class(c("gbt", "model"))
}
#' Summary method for the gbt function
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/gbt.html} for an example in Radiant
#'
#' @param object Return value from \code{\link{gbt}}
#' @param prn Print iteration history
#' @param ... further arguments passed to or from other methods
#'
#' @examples
#' result <- gbt(
#' titanic, "survived", c("pclass", "sex"),
#' early_stopping_rounds = 0, nthread = 1
#' )
#' summary(result)
#' @seealso \code{\link{gbt}} to generate results
#' @seealso \code{\link{plot.gbt}} to plot results
#' @seealso \code{\link{predict.gbt}} for prediction
#'
#' @export
summary.gbt <- function(object, prn = TRUE, ...) {
if (is.character(object)) {
return(object)
}
cat("Gradient Boosted Trees (XGBoost)\n")
if (object$type == "classification") {
cat("Type : Classification")
} else {
cat("Type : Regression")
}
cat("\nData :", object$df_name)
if (!is.empty(object$data_filter)) {
cat("\nFilter :", gsub("\\n", "", object$data_filter))
}
if (!is.empty(object$arr)) {
cat("\nArrange :", gsub("\\n", "", object$arr))
}
if (!is.empty(object$rows)) {
cat("\nSlice :", gsub("\\n", "", object$rows))
}
cat("\nResponse variable :", object$rvar)
if (object$type == "classification") {
cat("\nLevel :", object$lev, "in", object$rvar)
}
cat("\nExplanatory variables:", paste0(object$evar, collapse = ", "), "\n")
if (length(object$wtsname) > 0) {
cat("Weights used :", object$wtsname, "\n")
}
cat("Max depth :", object$max_depth, "\n")
cat("Learning rate (eta) :", object$learning_rate, "\n")
cat("Min split loss :", object$min_split_loss, "\n")
cat("Min child weight :", object$min_child_weight, "\n")
cat("Sub-sample :", object$subsample, "\n")
cat("Nr of rounds (trees) :", object$nrounds, "\n")
cat("Early stopping rounds:", object$early_stopping_rounds, "\n")
if (length(object$extra_args)) {
extra_args <- deparse(object$extra_args) %>%
sub("list\\(", "", .) %>%
sub("\\)$", "", .) %>%
sub(" {2,}", " ", .)
cat("Additional arguments :", extra_args, "\n")
}
if (!is.empty(object$seed)) {
cat("Seed :", object$seed, "\n")
}
if (!is.empty(object$wts, "None") && (length(unique(object$wts)) > 2 || min(object$wts) >= 1)) {
cat("Nr obs :", format_nr(sum(object$wts), dec = 0), "\n")
} else {
cat("Nr obs :", format_nr(object$nr_obs, dec = 0), "\n")
}
if (isTRUE(prn)) {
cat("\nIteration history:\n\n")
ih <- object$output[c(-2, -3)]
if (length(ih) > 20) ih <- c(head(ih, 10), "...", tail(ih, 10))
cat(paste0(ih, collapse = "\n"))
}
}
#' Plot method for the gbt function
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/gbt.html} for an example in Radiant
#'
#' @param x Return value from \code{\link{gbt}}
#' @param plots Plots to produce for the specified Gradient Boosted Tree model. Use "" to avoid showing any plots (default). Options are ...
#' @param nrobs Number of data points to show in scatter plots (-1 for all)
#' @param incl Which variables to include in a coefficient plot or PDP plot
#' @param incl_int Which interactions to investigate in PDP plots
#' @param shiny Did the function call originate inside a shiny app
#' @param custom Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned.
#' This option can be used to customize plots (e.g., add a title, change x and y labels, etc.).
#' See examples and \url{https://ggplot2.tidyverse.org} for options.
#' @param ... further arguments passed to or from other methods
#'
#' @examples
#' result <- gbt(
#' titanic, "survived", c("pclass", "sex"),
#' early_stopping_rounds = 0, nthread = 1
#' )
#' plot(result)
#'
#' @seealso \code{\link{gbt}} to generate results
#' @seealso \code{\link{summary.gbt}} to summarize results
#' @seealso \code{\link{predict.gbt}} for prediction
#'
#' @importFrom pdp partial
#' @importFrom rlang .data
#'
#' @export
plot.gbt <- function(x, plots = "", nrobs = Inf,
incl = NULL, incl_int = NULL,
shiny = FALSE, custom = FALSE, ...) {
if (is.character(x) || !inherits(x$model, "xgb.Booster")) {
return(x)
}
plot_list <- list()
ncol <- 1
if (x$type == "regression" && "dashboard" %in% plots) {
plot_list <- plot.regress(x, plots = "dashboard", lines = "line", nrobs = nrobs, custom = TRUE)
ncol <- 2
}
if ("pdp" %in% plots) {
ncol <- 2
if (length(incl) == 0 && length(incl_int) == 0) {
return("Select one or more variables to generate Partial Dependence Plots")
}
mod_dat <- x$model$model[, -1, drop = FALSE]
dtx <- onehot(mod_dat)[, -1, drop = FALSE]
for (pn in incl) {
if (is.factor(mod_dat[[pn]])) {
fn <- paste0(pn, levels(mod_dat[[pn]]))[-1]
effects <- rep(NA, length(fn))
nr <- length(fn)
for (i in seq_len(nr)) {
seed <- x$seed
dtx_cat <- dtx
dtx_cat[, setdiff(fn, fn[i])] <- 0
pdi <- pdp::partial(
x$model,
pred.var = fn[i], plot = FALSE,
prob = x$type == "classification", train = dtx_cat
)
effects[i] <- pdi[pdi[[1]] > 0, 2]
}
pgrid <- as.data.frame(matrix(0, ncol = nr))
colnames(pgrid) <- fn
base <- pdp::partial(
x$model,
pred.var = fn,
pred.grid = pgrid, plot = FALSE,
prob = x$type == "classification", train = dtx
)[1, "yhat"]
pd <- data.frame(label = levels(mod_dat[[pn]]), yhat = c(base, effects)) %>%
mutate(label = factor(label, levels = label))
colnames(pd)[1] <- pn
plot_list[[pn]] <- ggplot(pd, aes(x = .data[[pn]], y = .data$yhat)) +
geom_point() +
labs(y = NULL)
} else {
plot_list[[pn]] <- pdp::partial(
x$model,
pred.var = pn, plot = TRUE, rug = TRUE,
prob = x$type == "classification", plot.engine = "ggplot2",
train = dtx
) + labs(y = NULL)
}
}
for (pn_lab in incl_int) {
iint <- strsplit(pn_lab, ":")[[1]]
df <- mod_dat[, iint]
is_num <- sapply(df, is.numeric)
if (sum(is_num) == 2) {
# 2 numeric variables
cn <- colnames(df)
num_range1 <- df[[cn[1]]] %>%
(function(x) seq(min(x), max(x), length.out = 20)) %>%
paste0(collapse = ", ")
num_range2 <- df[[cn[2]]] %>%
(function(x) seq(min(x), max(x), length.out = 20)) %>%
paste0(collapse = ", ")
pred <- predict(x, pred_cmd = glue("{cn[1]} = c({num_range1}), {cn[2]} = c({num_range2})"))
plot_list[[pn_lab]] <- ggplot(pred, aes(x = .data[[cn[1]]], y = .data[[cn[2]]], fill = .data[["Prediction"]])) +
geom_tile()
} else if (sum(is_num) == 0) {
# 2 categorical variables
cn <- colnames(df)
pred <- predict(x, pred_cmd = glue("{cn[1]} = levels({cn[1]}), {cn[2]} = levels({cn[2]})"))
plot_list[[pn_lab]] <- visualize(
pred,
xvar = cn[1], yvar = "Prediction", type = "line", color = cn[2], custom = TRUE
) + labs(y = NULL)
} else if (sum(is_num) == 1) {
# 1 categorical and one numeric variable
cn <- colnames(df)
cn_fct <- cn[!is_num]
cn_num <- cn[is_num]
num_range <- df[[cn_num[1]]] %>%
(function(x) seq(min(x), max(x), length.out = 20)) %>%
paste0(collapse = ", ")
pred <- predict(x, pred_cmd = glue("{cn_num[1]} = c({num_range}), {cn_fct} = levels({cn_fct})"))
plot_list[[pn_lab]] <- plot(pred, xvar = cn_num[1], color = cn_fct, custom = TRUE)
}
}
}
if ("pred_plot" %in% plots) {
ncol <- 2
if (length(incl) > 0 | length(incl_int) > 0) {
plot_list <- pred_plot(x, plot_list, incl, incl_int, ...)
} else {
return("Select one or more variables to generate Prediction plots")
}
}
if ("vip" %in% plots) {
ncol <- 1
if (length(x$evar) < 2) {
message("Model must contain at least 2 explanatory variables (features). Permutation Importance plot cannot be generated")
} else {
vi_scores <- varimp(x)
plot_list[["vip"]] <-
visualize(vi_scores, yvar = "Importance", xvar = "Variable", type = "bar", custom = TRUE) +
labs(
title = "Permutation Importance",
x = NULL,
y = ifelse(x$type == "regression", "Importance (R-square decrease)", "Importance (AUC decrease)")
) +
coord_flip() +
theme(axis.text.y = element_text(hjust = 0))
}
}
if (length(plot_list) > 0) {
if (custom) {
if (length(plot_list) == 1) plot_list[[1]] else plot_list
} else {
patchwork::wrap_plots(plot_list, ncol = ncol) %>%
(function(x) if (isTRUE(shiny)) x else print(x))
}
}
}
#' Predict method for the gbt function
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/gbt.html} for an example in Radiant
#'
#' @param object Return value from \code{\link{gbt}}
#' @param pred_data Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation
#' @param pred_cmd Generate predictions using a command. For example, `pclass = levels(pclass)` would produce predictions for the different levels of factor `pclass`. To add another variable, create a vector of prediction strings, (e.g., c('pclass = levels(pclass)', 'age = seq(0,100,20)')
#' @param dec Number of decimals to show
#' @param envir Environment to extract data from
#' @param ... further arguments passed to or from other methods
#'
#' @examples
#' result <- gbt(
#' titanic, "survived", c("pclass", "sex"),
#' early_stopping_rounds = 2, nthread = 1
#' )
#' predict(result, pred_cmd = "pclass = levels(pclass)")
#' result <- gbt(diamonds, "price", "carat:color", type = "regression", nthread = 1)
#' predict(result, pred_cmd = "carat = 1:3")
#' predict(result, pred_data = diamonds) %>% head()
#' @seealso \code{\link{gbt}} to generate the result
#' @seealso \code{\link{summary.gbt}} to summarize results
#'
#' @export
predict.gbt <- function(object, pred_data = NULL, pred_cmd = "",
dec = 3, envir = parent.frame(), ...) {
if (is.character(object)) {
return(object)
}
## ensure you have a name for the prediction dataset
if (is.data.frame(pred_data)) {
df_name <- deparse(substitute(pred_data))
} else {
df_name <- pred_data
}
pfun <- function(model, pred, se, conf_lev) {
## ensure the factor levels in the prediction data are the
## same as in the data used for estimation
est_data <- model$model[, -1, drop = FALSE]
for (i in colnames(pred)) {
if (is.factor(est_data[[i]])) {
pred[[i]] <- factor(pred[[i]], levels = levels(est_data[[i]]))
}
}
pred <- onehot(pred[, colnames(est_data), drop = FALSE])[, -1, drop = FALSE]
## for testing purposes
# pred <- model$model[, -1, drop = FALSE]
pred_val <- try(sshhr(predict(model, pred)), silent = TRUE)
if (!inherits(pred_val, "try-error")) {
pred_val %<>% as.data.frame(stringsAsFactors = FALSE) %>%
select(1) %>%
set_colnames("Prediction")
}
pred_val
}
predict_model(object, pfun, "gbt.predict", pred_data, pred_cmd, conf_lev = 0.95, se = FALSE, dec, envir = envir) %>%
set_attr("radiant_pred_data", df_name)
}
#' Print method for predict.gbt
#'
#' @param x Return value from prediction method
#' @param ... further arguments passed to or from other methods
#' @param n Number of lines of prediction results to print. Use -1 to print all lines
#'
#' @export
print.gbt.predict <- function(x, ..., n = 10) {
print_predict_model(x, ..., n = n, header = "Gradiant Boosted Trees")
}
#' Cross-validation for Gradient Boosted Trees
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/gbt.html} for an example in Radiant
#'
#' @param object Object of type "gbt" or "ranger"
#' @param K Number of cross validation passes to use (aka nfold)
#' @param repeats Repeated cross validation
#' @param params List of parameters (see XGBoost documentation)
#' @param nrounds Number of trees to create
#' @param early_stopping_rounds Early stopping rule
#' @param nthread Number of parallel threads to use. Defaults to 12 if available
#' @param train An optional xgb.DMatrix object containing the original training data. Not needed when using Radiant's gbt function
#' @param type Model type ("classification" or "regression")
#' @param trace Print progress
#' @param seed Random seed to use as the starting point
#' @param maximize When a custom function is used, xgb.cv requires the user indicate if the function output should be maximized (TRUE) or minimized (FALSE)
#' @param fun Function to use for model evaluation (i.e., auc for classification and RMSE for regression)
#' @param ... Additional arguments to be passed to 'fun'
#'
#' @return A data.frame sorted by the mean of the performance metric
#'
#' @seealso \code{\link{gbt}} to generate an initial model that can be passed to cv.gbt
#' @seealso \code{\link{Rsq}} to calculate an R-squared measure for a regression
#' @seealso \code{\link{RMSE}} to calculate the Root Mean Squared Error for a regression
#' @seealso \code{\link{MAE}} to calculate the Mean Absolute Error for a regression
#' @seealso \code{\link{auc}} to calculate the area under the ROC curve for classification
#' @seealso \code{\link{profit}} to calculate profits for classification at a cost/margin threshold
#'
#' @importFrom shiny getDefaultReactiveDomain withProgress incProgress
#'
#' @examples
#' \dontrun{
#' result <- gbt(dvd, "buy", c("coupon", "purch", "last"))
#' cv.gbt(result, params = list(max_depth = 1:6))
#' cv.gbt(result, params = list(max_depth = 1:6), fun = "logloss")
#' cv.gbt(
#' result,
#' params = list(learning_rate = seq(0.1, 1.0, 0.1)),
#' maximize = TRUE, fun = profit, cost = 1, margin = 5
#' )
#' result <- gbt(diamonds, "price", c("carat", "color", "clarity"), type = "regression")
#' cv.gbt(result, params = list(max_depth = 1:2, min_child_weight = 1:2))
#' cv.gbt(result, params = list(learning_rate = seq(0.1, 0.5, 0.1)), fun = Rsq, maximize = TRUE)
#' cv.gbt(result, params = list(learning_rate = seq(0.1, 0.5, 0.1)), fun = MAE, maximize = FALSE)
#' }
#'
#' @export
cv.gbt <- function(object, K = 5, repeats = 1, params = list(),
nrounds = 500, early_stopping_rounds = 10, nthread = 12,
train = NULL, type = "classification",
trace = TRUE, seed = 1234, maximize = NULL, fun, ...) {
if (inherits(object, "gbt")) {
dv <- object$rvar
dataset <- object$model$model
dtx <- onehot(dataset[, -1, drop = FALSE])[, -1, drop = FALSE]
type <- object$type
if (type == "classification") {
objective <- "binary:logistic"
dty <- as.integer(dataset[[dv]] == object$lev)
} else {
objective <- "reg:squarederror"
dty <- dataset[[dv]]
}
train <- xgboost::xgb.DMatrix(data = dtx, label = dty)
params_base <- object$model$params
if (is.empty(params_base[["eval_metric"]])) {
params_base[["eval_metric"]] <- object$extra_args[["eval_metric"]]
}
if (is.empty(params_base[["maximize"]])) {
params_base[["maximize"]] <- object$extra_args[["maximize"]]
}
} else if (!inherits(object, "xgb.Booster")) {
stop("The model object does not seems to be a Gradient Boosted Tree")
} else {
if (!inherits(train, "xgb.DMatrix")) {
train <- eval(object$call[["data"]])
}
params_base <- object$params
}
if (!inherits(train, "xgb.DMatrix")) {
stop("Could not access data. Please use the 'train' argument to pass along a matrix created using xgboost::xgb.DMatrix")
}
params_base[c("nrounds", "nthread", "silent")] <- NULL
for (n in names(params)) {
params_base[[n]] <- params[[n]]
}
params <- params_base
if (is.empty(maximize)) {
maximize <- params$maximize
}
if (missing(fun)) {
if (type == "classification") {
if (length(params$eval_metric) == 0) {
fun <- params$eval_metric <- "auc"
} else if (is.character(params$eval_metric)) {
fun <- params$eval_metric
} else {
fun <- list("custom" = params$eval_metric)
}
} else {
if (length(params$eval_metric) == 0) {
fun <- params$eval_metric <- "rmse"
} else if (is.character(params$eval_metric)) {
fun <- params$eval_metric
} else {
fun <- list("custom" = params$eval_metric)
}
}
}
if (length(shiny::getDefaultReactiveDomain()) > 0) {
trace <- FALSE
incProgress <- shiny::incProgress
withProgress <- shiny::withProgress
} else {
incProgress <- function(...) {}
withProgress <- function(...) list(...)[["expr"]]
}
## setting up a customer evaluation function
if (is.function(fun)) {
if (missing(...)) {
if (type == "classification") {
fun_wrapper <- function(preds, dtrain) {
labels <- xgboost::getinfo(dtrain, "label")
value <- fun(preds, labels, 1)
list(metric = cn, value = value)
}
} else {
fun_wrapper <- function(preds, dtrain) {
labels <- xgboost::getinfo(dtrain, "label")
value <- fun(preds, labels)
list(metric = cn, value = value)
}
}
} else {
if (type == "classification") {
fun_wrapper <- function(preds, dtrain) {
labels <- xgboost::getinfo(dtrain, "label")
value <- fun(preds, labels, 1, ...)
list(metric = cn, value = value)
}
} else {
fun_wrapper <- function(preds, dtrain) {
labels <- xgboost::getinfo(dtrain, "label")
value <- fun(preds, labels, ...)
list(metric = cn, value = value)
}
}
}
cn <- deparse(substitute(fun))
if (grepl(":{2,3}", cn)) cn <- sub("^.+:{2,3}", "", cn)
params$eval_metric <- cn
} else if (is.list(fun)) {
fun_wrapper <- fun[["custom"]]
params$eval_metric <- "custom"
} else {
fun_wrapper <- params$eval_metric <- fun
}
tf <- tempfile()
tune_grid <- expand.grid(params)
nitt <- nrow(tune_grid)
withProgress(message = "Running cross-validation (gbt)", value = 0, {
out <- list()
for (i in seq_len(nitt)) {
cv_params <- tune_grid[i, ]
if (!is.empty(cv_params$nrounds)) {
nrounds <- cv_params$nrounds
cv_params$nrounds <- NULL
}
if (trace) {
cat("Working on", paste0(paste(colnames(cv_params), "=", cv_params), collapse = ", "), "\n")
}
for (j in seq_len(repeats)) {
set.seed(seed)
sink(tf) ## avoiding messages from xgboost::xgb.cv
cv_params_tmp <- cv_params
for (nm in c("eval_metric", "maximize", "early_stopping_rounds", "nthread")) {
cv_params_tmp[[nm]] <- NULL
}
model <- try(xgboost::xgb.cv(
params = as.list(cv_params_tmp),
data = train,
nfold = K,
print_every_n = 500,
eval_metric = fun_wrapper,
maximize = maximize,
early_stopping_rounds = early_stopping_rounds,
nrounds = nrounds,
nthread = nthread
))
sink()
if (inherits(model, "try-error")) {
stop(model)
}
out[[paste0(i, "-", j)]] <- as.data.frame(c(
nrounds = nrounds, best_iteration = model$best_iteration,
model$evaluation_log[model$best_iteration, -1], cv_params
))
}
incProgress(1 / nitt, detail = paste("\nCompleted run", i, "out of", nitt))
}
})
out <- bind_rows(out)
if (type == "classification") {
out[order(out[[5]], decreasing = TRUE), ]
} else {
out[order(out[[5]], decreasing = FALSE), ]
}
}