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lgb.cv.R
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lgb.cv.R
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#' @importFrom R6 R6Class
CVBooster <- R6::R6Class(
classname = "lgb.CVBooster",
cloneable = FALSE,
public = list(
best_iter = -1L,
best_score = NA,
record_evals = list(),
boosters = list(),
initialize = function(x) {
self$boosters <- x
return(invisible(NULL))
},
reset_parameter = function(new_params) {
for (x in self$boosters) {
x[["booster"]]$reset_parameter(params = new_params)
}
return(invisible(self))
}
)
)
#' @name lgb.cv
#' @title Main CV logic for LightGBM
#' @description Cross validation logic used by LightGBM
#' @inheritParams lgb_shared_params
#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
#' @param label Deprecated. See "Deprecated Arguments" section below.
#' @param weight Deprecated. See "Deprecated Arguments" section below.
#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals}
#' @param showsd \code{boolean}, whether to show standard deviation of cross validation.
#' This parameter defaults to \code{TRUE}. Setting it to \code{FALSE} can lead to a
#' slight speedup by avoiding unnecessary computation.
#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
#' by the values of outcome labels.
#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
#' (each element must be a vector of test fold's indices). When folds are supplied,
#' the \code{nfold} and \code{stratified} parameters are ignored.
#' @param colnames Deprecated. See "Deprecated Arguments" section below.
#' @param categorical_feature Deprecated. See "Deprecated Arguments" section below.
#' @param callbacks List of callback functions that are applied at each iteration.
#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the booster model
#' into a predictor model which frees up memory and the original datasets
#' @param eval_train_metric \code{boolean}, whether to add the cross validation results on the
#' training data. This parameter defaults to \code{FALSE}. Setting it to \code{TRUE}
#' will increase run time.
#' @inheritSection lgb_shared_params Early Stopping
#' @return a trained model \code{lgb.CVBooster}.
#'
#' @examples
#' \donttest{
#' \dontshow{setLGBMthreads(2L)}
#' \dontshow{data.table::setDTthreads(1L)}
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' params <- list(
#' objective = "regression"
#' , metric = "l2"
#' , min_data = 1L
#' , learning_rate = 1.0
#' , num_threads = 2L
#' )
#' model <- lgb.cv(
#' params = params
#' , data = dtrain
#' , nrounds = 5L
#' , nfold = 3L
#' )
#' }
#'
#' @section Deprecated Arguments:
#'
#' A future release of \code{lightgbm} will require passing an \code{lgb.Dataset}
#' to argument \code{'data'}. It will also remove support for passing arguments
#' \code{'categorical_feature'}, \code{'colnames'}, \code{'label'}, and \code{'weight'}.
#'
#' @importFrom data.table data.table setorderv
#' @export
lgb.cv <- function(params = list()
, data
, nrounds = 100L
, nfold = 3L
, label = NULL
, weight = NULL
, obj = NULL
, eval = NULL
, verbose = 1L
, record = TRUE
, eval_freq = 1L
, showsd = TRUE
, stratified = TRUE
, folds = NULL
, init_model = NULL
, colnames = NULL
, categorical_feature = NULL
, early_stopping_rounds = NULL
, callbacks = list()
, reset_data = FALSE
, serializable = TRUE
, eval_train_metric = FALSE
) {
if (nrounds <= 0L) {
stop("nrounds should be greater than zero")
}
# If 'data' is not an lgb.Dataset, try to construct one using 'label'
if (!.is_Dataset(x = data)) {
warning(paste0(
"Passing anything other than an lgb.Dataset object to lgb.cv() is deprecated. "
, "Either pass an lgb.Dataset object, or use lightgbm()."
))
if (is.null(label)) {
stop("'label' must be provided for lgb.cv if 'data' is not an 'lgb.Dataset'")
}
data <- lgb.Dataset(data = data, label = label)
}
# raise deprecation warnings if necessary
# ref: https://github.com/microsoft/LightGBM/issues/6435
args <- names(match.call())
if ("categorical_feature" %in% args) {
.emit_dataset_kwarg_warning("categorical_feature", "lgb.cv")
}
if ("colnames" %in% args) {
.emit_dataset_kwarg_warning("colnames", "lgb.cv")
}
if ("label" %in% args) {
.emit_dataset_kwarg_warning("label", "lgb.cv")
}
if ("weight" %in% args) {
.emit_dataset_kwarg_warning("weight", "lgb.cv")
}
# set some parameters, resolving the way they were passed in with other parameters
# in `params`.
# this ensures that the model stored with Booster$save() correctly represents
# what was passed in
params <- .check_wrapper_param(
main_param_name = "verbosity"
, params = params
, alternative_kwarg_value = verbose
)
params <- .check_wrapper_param(
main_param_name = "num_iterations"
, params = params
, alternative_kwarg_value = nrounds
)
params <- .check_wrapper_param(
main_param_name = "metric"
, params = params
, alternative_kwarg_value = NULL
)
params <- .check_wrapper_param(
main_param_name = "objective"
, params = params
, alternative_kwarg_value = obj
)
params <- .check_wrapper_param(
main_param_name = "early_stopping_round"
, params = params
, alternative_kwarg_value = early_stopping_rounds
)
early_stopping_rounds <- params[["early_stopping_round"]]
# extract any function objects passed for objective or metric
fobj <- NULL
if (is.function(params$objective)) {
fobj <- params$objective
params$objective <- "none"
}
# If eval is a single function, store it as a 1-element list
# (for backwards compatibility). If it is a list of functions, store
# all of them. This makes it possible to pass any mix of strings like "auc"
# and custom functions to eval
params <- .check_eval(params = params, eval = eval)
eval_functions <- list(NULL)
if (is.function(eval)) {
eval_functions <- list(eval)
}
if (methods::is(eval, "list")) {
eval_functions <- Filter(
f = is.function
, x = eval
)
}
# Init predictor to empty
predictor <- NULL
# Check for boosting from a trained model
if (is.character(init_model)) {
predictor <- Predictor$new(modelfile = init_model)
} else if (.is_Booster(x = init_model)) {
predictor <- init_model$to_predictor()
}
# Set the iteration to start from / end to (and check for boosting from a trained model, again)
begin_iteration <- 1L
if (!is.null(predictor)) {
begin_iteration <- predictor$current_iter() + 1L
}
end_iteration <- begin_iteration + params[["num_iterations"]] - 1L
# pop interaction_constraints off of params. It needs some preprocessing on the
# R side before being passed into the Dataset object
interaction_constraints <- params[["interaction_constraints"]]
params["interaction_constraints"] <- NULL
# Construct datasets, if needed
data$update_params(params = params)
data$construct()
# Check interaction constraints
cnames <- NULL
if (!is.null(colnames)) {
cnames <- colnames
} else if (!is.null(data$get_colnames())) {
cnames <- data$get_colnames()
}
params[["interaction_constraints"]] <- .check_interaction_constraints(
interaction_constraints = interaction_constraints
, column_names = cnames
)
if (!is.null(weight)) {
data$set_field(field_name = "weight", data = weight)
}
# Update parameters with parsed parameters
data$update_params(params = params)
# Create the predictor set
data$.__enclos_env__$private$set_predictor(predictor = predictor)
# Write column names
if (!is.null(colnames)) {
data$set_colnames(colnames = colnames)
}
# Write categorical features
if (!is.null(categorical_feature)) {
data$set_categorical_feature(categorical_feature = categorical_feature)
}
if (!is.null(folds)) {
# Check for list of folds or for single value
if (!identical(class(folds), "list") || length(folds) < 2L) {
stop(sQuote("folds"), " must be a list with 2 or more elements that are vectors of indices for each CV-fold")
}
} else {
if (nfold <= 1L) {
stop(sQuote("nfold"), " must be > 1")
}
# Create folds
folds <- .generate_cv_folds(
nfold = nfold
, nrows = nrow(data)
, stratified = stratified
, label = get_field(dataset = data, field_name = "label")
, group = get_field(dataset = data, field_name = "group")
, params = params
)
}
# Add printing log callback
if (params[["verbosity"]] > 0L && eval_freq > 0L) {
callbacks <- .add_cb(cb_list = callbacks, cb = cb_print_evaluation(period = eval_freq))
}
# Add evaluation log callback
if (record) {
callbacks <- .add_cb(cb_list = callbacks, cb = cb_record_evaluation())
}
# Did user pass parameters that indicate they want to use early stopping?
using_early_stopping <- !is.null(early_stopping_rounds) && early_stopping_rounds > 0L
boosting_param_names <- .PARAMETER_ALIASES()[["boosting"]]
using_dart <- any(
sapply(
X = boosting_param_names
, FUN = function(param) {
identical(params[[param]], "dart")
}
)
)
# Cannot use early stopping with 'dart' boosting
if (using_dart) {
if (using_early_stopping) {
warning("Early stopping is not available in 'dart' mode.")
}
using_early_stopping <- FALSE
# Remove the cb_early_stop() function if it was passed in to callbacks
callbacks <- Filter(
f = function(cb_func) {
!identical(attr(cb_func, "name"), "cb_early_stop")
}
, x = callbacks
)
}
# If user supplied early_stopping_rounds, add the early stopping callback
if (using_early_stopping) {
callbacks <- .add_cb(
cb_list = callbacks
, cb = cb_early_stop(
stopping_rounds = early_stopping_rounds
, first_metric_only = isTRUE(params[["first_metric_only"]])
, verbose = params[["verbosity"]] > 0L
)
)
}
cb <- .categorize_callbacks(cb_list = callbacks)
# Construct booster for each fold. The data.table() code below is used to
# guarantee that indices are sorted while keeping init_score and weight together
# with the correct indices. Note that it takes advantage of the fact that
# someDT$some_column returns NULL is 'some_column' does not exist in the data.table
bst_folds <- lapply(
X = seq_along(folds)
, FUN = function(k) {
# For learning-to-rank, each fold is a named list with two elements:
# * `fold` = an integer vector of row indices
# * `group` = an integer vector describing which groups are in the fold
# For classification or regression tasks, it will just be an integer
# vector of row indices
folds_have_group <- "group" %in% names(folds[[k]])
if (folds_have_group) {
test_indices <- folds[[k]]$fold
test_group_indices <- folds[[k]]$group
test_groups <- get_field(dataset = data, field_name = "group")[test_group_indices]
train_groups <- get_field(dataset = data, field_name = "group")[-test_group_indices]
} else {
test_indices <- folds[[k]]
}
train_indices <- seq_len(nrow(data))[-test_indices]
# set up test set
indexDT <- data.table::data.table(
indices = test_indices
, weight = get_field(dataset = data, field_name = "weight")[test_indices]
, init_score = get_field(dataset = data, field_name = "init_score")[test_indices]
)
data.table::setorderv(x = indexDT, cols = "indices", order = 1L)
dtest <- lgb.slice.Dataset(data, indexDT$indices)
set_field(dataset = dtest, field_name = "weight", data = indexDT$weight)
set_field(dataset = dtest, field_name = "init_score", data = indexDT$init_score)
# set up training set
indexDT <- data.table::data.table(
indices = train_indices
, weight = get_field(dataset = data, field_name = "weight")[train_indices]
, init_score = get_field(dataset = data, field_name = "init_score")[train_indices]
)
data.table::setorderv(x = indexDT, cols = "indices", order = 1L)
dtrain <- lgb.slice.Dataset(data, indexDT$indices)
set_field(dataset = dtrain, field_name = "weight", data = indexDT$weight)
set_field(dataset = dtrain, field_name = "init_score", data = indexDT$init_score)
if (folds_have_group) {
set_field(dataset = dtest, field_name = "group", data = test_groups)
set_field(dataset = dtrain, field_name = "group", data = train_groups)
}
booster <- Booster$new(params = params, train_set = dtrain)
if (isTRUE(eval_train_metric)) {
booster$add_valid(data = dtrain, name = "train")
}
booster$add_valid(data = dtest, name = "valid")
return(
list(booster = booster)
)
}
)
# Create new booster
cv_booster <- CVBooster$new(x = bst_folds)
# Callback env
env <- CB_ENV$new()
env$model <- cv_booster
env$begin_iteration <- begin_iteration
env$end_iteration <- end_iteration
# Start training model using number of iterations to start and end with
for (i in seq.int(from = begin_iteration, to = end_iteration)) {
# Overwrite iteration in environment
env$iteration <- i
env$eval_list <- list()
for (f in cb$pre_iter) {
f(env)
}
# Update one boosting iteration
msg <- lapply(cv_booster$boosters, function(fd) {
fd$booster$update(fobj = fobj)
out <- list()
for (eval_function in eval_functions) {
out <- append(out, fd$booster$eval_valid(feval = eval_function))
}
return(out)
})
# Prepare collection of evaluation results
merged_msg <- .merge_cv_result(
msg = msg
, showsd = showsd
)
# Write evaluation result in environment
env$eval_list <- merged_msg$eval_list
# Check for standard deviation requirement
if (showsd) {
env$eval_err_list <- merged_msg$eval_err_list
}
# Loop through env
for (f in cb$post_iter) {
f(env)
}
# Check for early stopping and break if needed
if (env$met_early_stop) break
}
# When early stopping is not activated, we compute the best iteration / score ourselves
# based on the first first metric
if (record && is.na(env$best_score)) {
# when using a custom eval function, the metric name is returned from the
# function, so figure it out from record_evals
if (!is.null(eval_functions[1L])) {
first_metric <- names(cv_booster$record_evals[["valid"]])[1L]
} else {
first_metric <- cv_booster$.__enclos_env__$private$eval_names[1L]
}
.find_best <- which.min
if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) {
.find_best <- which.max
}
cv_booster$best_iter <- unname(
.find_best(
unlist(
cv_booster$record_evals[["valid"]][[first_metric]][[.EVAL_KEY()]]
)
)
)
cv_booster$best_score <- cv_booster$record_evals[["valid"]][[first_metric]][[.EVAL_KEY()]][[cv_booster$best_iter]]
}
# Propagate the best_iter attribute from the cv_booster to the individual boosters
for (bst in cv_booster$boosters) {
bst$booster$best_iter <- cv_booster$best_iter
}
if (reset_data) {
lapply(cv_booster$boosters, function(fd) {
# Store temporarily model data elsewhere
booster_old <- list(
best_iter = fd$booster$best_iter
, best_score = fd$booster$best_score
, record_evals = fd$booster$record_evals
)
# Reload model
fd$booster <- lgb.load(model_str = fd$booster$save_model_to_string())
fd$booster$best_iter <- booster_old$best_iter
fd$booster$best_score <- booster_old$best_score
fd$booster$record_evals <- booster_old$record_evals
})
}
if (serializable) {
lapply(cv_booster$boosters, function(model) model$booster$save_raw())
}
return(cv_booster)
}
# Generates random (stratified if needed) CV folds
.generate_cv_folds <- function(nfold, nrows, stratified, label, group, params) {
# Check for group existence
if (is.null(group)) {
# Shuffle
rnd_idx <- sample.int(nrows)
# Request stratified folds
if (isTRUE(stratified) && params$objective %in% c("binary", "multiclass") && length(label) == length(rnd_idx)) {
y <- label[rnd_idx]
y <- as.factor(y)
folds <- .stratified_folds(y = y, k = nfold)
} else {
# Make simple non-stratified folds
folds <- list()
# Loop through each fold
for (i in seq_len(nfold)) {
kstep <- length(rnd_idx) %/% (nfold - i + 1L)
folds[[i]] <- rnd_idx[seq_len(kstep)]
rnd_idx <- rnd_idx[-seq_len(kstep)]
}
}
} else {
# When doing group, stratified is not possible (only random selection)
if (nfold > length(group)) {
stop("\nYou requested too many folds for the number of available groups.\n")
}
# Degroup the groups
ungrouped <- inverse.rle(list(lengths = group, values = seq_along(group)))
# Can't stratify, shuffle
rnd_idx <- sample.int(length(group))
# Make simple non-stratified folds
folds <- list()
# Loop through each fold
for (i in seq_len(nfold)) {
kstep <- length(rnd_idx) %/% (nfold - i + 1L)
folds[[i]] <- list(
fold = which(ungrouped %in% rnd_idx[seq_len(kstep)])
, group = rnd_idx[seq_len(kstep)]
)
rnd_idx <- rnd_idx[-seq_len(kstep)]
}
}
return(folds)
}
# Creates CV folds stratified by the values of y.
# It was borrowed from caret::createFolds and simplified
# by always returning an unnamed list of fold indices.
#' @importFrom stats quantile
.stratified_folds <- function(y, k) {
# Group the numeric data based on their magnitudes
# and sample within those groups.
# When the number of samples is low, we may have
# issues further slicing the numeric data into
# groups. The number of groups will depend on the
# ratio of the number of folds to the sample size.
# At most, we will use quantiles. If the sample
# is too small, we just do regular unstratified CV
if (is.numeric(y)) {
cuts <- length(y) %/% k
if (cuts < 2L) {
cuts <- 2L
}
if (cuts > 5L) {
cuts <- 5L
}
y <- cut(
y
, unique(stats::quantile(y, probs = seq.int(0.0, 1.0, length.out = cuts)))
, include.lowest = TRUE
)
}
if (k < length(y)) {
# Reset levels so that the possible levels and
# the levels in the vector are the same
y <- as.factor(as.character(y))
numInClass <- table(y)
foldVector <- vector(mode = "integer", length(y))
# For each class, balance the fold allocation as far
# as possible, then resample the remainder.
# The final assignment of folds is also randomized.
for (i in seq_along(numInClass)) {
# Create a vector of integers from 1:k as many times as possible without
# going over the number of samples in the class. Note that if the number
# of samples in a class is less than k, nothing is produced here.
seqVector <- rep(seq_len(k), numInClass[i] %/% k)
# Add enough random integers to get length(seqVector) == numInClass[i]
if (numInClass[i] %% k > 0L) {
seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
}
# Shuffle the integers for fold assignment and assign to this classes's data
foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)
}
} else {
foldVector <- seq(along = y)
}
out <- split(seq(along = y), foldVector)
names(out) <- NULL
return(out)
}
.merge_cv_result <- function(msg, showsd) {
if (length(msg) == 0L) {
stop("lgb.cv: size of cv result error")
}
eval_len <- length(msg[[1L]])
if (eval_len == 0L) {
stop("lgb.cv: should provide at least one metric for CV")
}
# Get evaluation results using a list apply
eval_result <- lapply(seq_len(eval_len), function(j) {
as.numeric(lapply(seq_along(msg), function(i) {
msg[[i]][[j]]$value }))
})
# Get evaluation. Just taking the first element here to
# get structure (name, higher_better, data_name)
ret_eval <- msg[[1L]]
for (j in seq_len(eval_len)) {
ret_eval[[j]]$value <- mean(eval_result[[j]])
}
ret_eval_err <- NULL
# Check for standard deviation
if (showsd) {
# Parse standard deviation
for (j in seq_len(eval_len)) {
ret_eval_err <- c(
ret_eval_err
, sqrt(mean(eval_result[[j]] ^ 2L) - mean(eval_result[[j]]) ^ 2L)
)
}
ret_eval_err <- as.list(ret_eval_err)
}
return(
list(
eval_list = ret_eval
, eval_err_list = ret_eval_err
)
)
}