Nothing
R/LearnerClassifXgboost.R
In mlr3learners: Recommended Learners for 'mlr3'
#' @title Extreme Gradient Boosting Classification Learner
#'
#' @name mlr_learners_classif.xgboost
#'
#' @description
#' eXtreme Gradient Boosting classification.
#' Calls [xgboost::xgb.train()] from package \CRANpkg{xgboost}.
#'
#' If not specified otherwise, the evaluation metric is set to the default `"logloss"`
#' for binary classification problems and set to `"mlogloss"` for multiclass problems.
#' This was necessary to silence a deprecation warning.
#'
#' Note that using the `watchlist` parameter directly will lead to problems when wrapping this [`Learner`] in a
#' `mlr3pipelines` `GraphLearner` as the preprocessing steps will not be applied to the data in the watchlist.
#'
#' @template note_xgboost
#' @section Initial parameter values:
#' - `nrounds`:
#' - Actual default: no default.
#' - Adjusted default: 1.
#' - Reason for change: Without a default construction of the learner
#' would error. Just setting a nonsense default to workaround this.
#' `nrounds` needs to be tuned by the user.
#' - `nthread`:
#' - Actual value: Undefined, triggering auto-detection of the number of CPUs.
#' - Adjusted value: 1.
#' - Reason for change: Conflicting with parallelization via \CRANpkg{future}.
#' - `verbose`:
#' - Actual default: 1.
#' - Adjusted default: 0.
#' - Reason for change: Reduce verbosity.
#'
#' @section Early stopping:
#' Early stopping can be used to find the optimal number of boosting rounds.
#' The `early_stopping_set` parameter controls which set is used to monitor the performance.
#' Set `early_stopping_set = "test"` to monitor the performance of the model on the test set while training.
#' The test set for early stopping can be set with the `"test"` row role in the [mlr3::Task].
#' Additionally, the range must be set in which the performance must increase with `early_stopping_rounds` and the maximum number of boosting rounds with `nrounds`.
#' While resampling, the test set is automatically applied from the [mlr3::Resampling].
#' Not that using the test set for early stopping can potentially bias the performance scores.
#' See the section on early stopping in the examples.
#'
#' @templateVar id classif.xgboost
#' @template learner
#'
#' @references
#' `r format_bib("chen_2016")`
#'
#' @export
#' @template seealso_learner
#' @template example_dontrun
#' @examples
#'
#' \dontrun{
#' # Train learner with early stopping on spam data set
#' task = tsk("spam")
#'
#' # Split task into training and test set
#' split = partition(task, ratio = 0.8)
#' task$set_row_roles(split$test, "test")
#'
#' # Set early stopping parameter
#' learner = lrn("classif.xgboost",
#' nrounds = 100,
#' early_stopping_rounds = 10,
#' early_stopping_set = "test"
#' )
#'
#' # Train learner with early stopping
#' learner$train(task)
#' }
LearnerClassifXgboost = R6Class("LearnerClassifXgboost",
inherit = LearnerClassif,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
alpha = p_dbl(0, default = 0, tags = "train"),
approxcontrib = p_lgl(default = FALSE, tags = "predict"),
base_score = p_dbl(default = 0.5, tags = "train"),
booster = p_fct(c("gbtree", "gblinear", "dart"), default = "gbtree", tags = c("train", "control")),
callbacks = p_uty(default = list(), tags = "train"),
colsample_bylevel = p_dbl(0, 1, default = 1, tags = "train"),
colsample_bynode = p_dbl(0, 1, default = 1, tags = "train"),
colsample_bytree = p_dbl(0, 1, default = 1, tags = c("train", "control")),
device = p_uty(default = "cpu", tags = "train"),
disable_default_eval_metric = p_lgl(default = FALSE, tags = "train"),
early_stopping_rounds = p_int(1L, default = NULL, special_vals = list(NULL), tags = "train"),
early_stopping_set = p_fct(c("none", "train", "test"), default = "none", tags = "train"),
eta = p_dbl(0, 1, default = 0.3, tags = c("train", "control")),
eval_metric = p_uty(tags = "train"),
feature_selector = p_fct(c("cyclic", "shuffle", "random", "greedy", "thrifty"), default = "cyclic", tags = "train"),
feval = p_uty(default = NULL, tags = "train"),
gamma = p_dbl(0, default = 0, tags = c("train", "control")),
grow_policy = p_fct(c("depthwise", "lossguide"), default = "depthwise", tags = "train"),
interaction_constraints = p_uty(tags = "train"),
iterationrange = p_uty(tags = "predict"),
lambda = p_dbl(0, default = 1, tags = "train"),
lambda_bias = p_dbl(0, default = 0, tags = "train"),
max_bin = p_int(2L, default = 256L, tags = "train"),
max_delta_step = p_dbl(0, default = 0, tags = "train"),
max_depth = p_int(0L, default = 6L, tags = c("train", "control")),
max_leaves = p_int(0L, default = 0L, tags = "train"),
maximize = p_lgl(default = NULL, special_vals = list(NULL), tags = "train"),
min_child_weight = p_dbl(0, default = 1, tags = c("train", "control")),
missing = p_dbl(default = NA, tags = c("train", "predict"), special_vals = list(NA, NA_real_, NULL)),
monotone_constraints = p_uty(default = 0, tags = c("train", "control"), custom_check = crate(function(x) { checkmate::check_integerish(x, lower = -1, upper = 1, any.missing = FALSE) })), # nolint
normalize_type = p_fct(c("tree", "forest"), default = "tree", tags = "train"),
nrounds = p_int(1L, tags = c("train", "hotstart")),
nthread = p_int(1L, default = 1L, tags = c("train", "control", "threads")),
ntreelimit = p_int(1L, default = NULL, special_vals = list(NULL), tags = "predict"),
num_parallel_tree = p_int(1L, default = 1L, tags = c("train", "control")),
objective = p_uty(default = "binary:logistic", tags = c("train", "predict", "control")),
one_drop = p_lgl(default = FALSE, tags = "train"),
outputmargin = p_lgl(default = FALSE, tags = "predict"),
predcontrib = p_lgl(default = FALSE, tags = "predict"),
predinteraction = p_lgl(default = FALSE, tags = "predict"),
predleaf = p_lgl(default = FALSE, tags = "predict"),
print_every_n = p_int(1L, default = 1L, tags = "train"),
process_type = p_fct(c("default", "update"), default = "default", tags = "train"),
rate_drop = p_dbl(0, 1, default = 0, tags = "train"),
refresh_leaf = p_lgl(default = TRUE, tags = "train"),
reshape = p_lgl(default = FALSE, tags = "predict"),
seed_per_iteration = p_lgl(default = FALSE, tags = "train"),
sampling_method = p_fct(c("uniform", "gradient_based"), default = "uniform", tags = "train"),
sample_type = p_fct(c("uniform", "weighted"), default = "uniform", tags = "train"),
save_name = p_uty(default = NULL, tags = "train"),
save_period = p_int(0, default = NULL, special_vals = list(NULL), tags = "train"),
scale_pos_weight = p_dbl(default = 1, tags = "train"),
skip_drop = p_dbl(0, 1, default = 0, tags = "train"),
strict_shape = p_lgl(default = FALSE, tags = "predict"),
subsample = p_dbl(0, 1, default = 1, tags = c("train", "control")),
top_k = p_int(0, default = 0, tags = "train"),
training = p_lgl(default = FALSE, tags = "predict"),
tree_method = p_fct(c("auto", "exact", "approx", "hist", "gpu_hist"), default = "auto", tags = "train"),
tweedie_variance_power = p_dbl(1, 2, default = 1.5, tags = "train"),
updater = p_uty(tags = "train"), # Default depends on the selected booster
verbose = p_int(0L, 2L, default = 1L, tags = "train"),
watchlist = p_uty(default = NULL, tags = "train"),
xgb_model = p_uty(default = NULL, tags = "train")
)
# param deps
ps$add_dep("tweedie_variance_power", "objective", CondEqual$new("reg:tweedie"))
ps$add_dep("print_every_n", "verbose", CondEqual$new(1L))
ps$add_dep("sampling_method", "booster", CondEqual$new("gbtree"))
ps$add_dep("normalize_type", "booster", CondEqual$new("dart"))
ps$add_dep("rate_drop", "booster", CondEqual$new("dart"))
ps$add_dep("skip_drop", "booster", CondEqual$new("dart"))
ps$add_dep("one_drop", "booster", CondEqual$new("dart"))
ps$add_dep("sample_type", "booster", CondEqual$new("dart"))
ps$add_dep("tree_method", "booster", CondAnyOf$new(c("gbtree", "dart")))
ps$add_dep("grow_policy", "tree_method", CondEqual$new("hist"))
ps$add_dep("max_leaves", "grow_policy", CondEqual$new("lossguide"))
ps$add_dep("max_bin", "tree_method", CondEqual$new("hist"))
ps$add_dep("feature_selector", "booster", CondEqual$new("gblinear"))
ps$add_dep("top_k", "booster", CondEqual$new("gblinear"))
ps$add_dep("top_k", "feature_selector", CondAnyOf$new(c("greedy", "thrifty")))
ps$add_dep("lambda_bias", "booster", CondEqual$new("gblinear"))
# custom defaults
ps$values = list(nrounds = 1L, nthread = 1L, verbose = 0L, early_stopping_set = "none")
super$initialize(
id = "classif.xgboost",
predict_types = c("response", "prob"),
param_set = ps,
feature_types = c("logical", "integer", "numeric"),
properties = c("weights", "missings", "twoclass", "multiclass", "importance", "hotstart_forward"),
packages = c("mlr3learners", "xgboost"),
label = "Extreme Gradient Boosting",
man = "mlr3learners::mlr_learners_classif.xgboost"
)
},
#' @description
#' The importance scores are calculated with [xgboost::xgb.importance()].
#'
#' @return Named `numeric()`.
importance = function() {
if (is.null(self$model)) {
stopf("No model stored")
}
imp = xgboost::xgb.importance(
model = self$model
)
set_names(imp$Gain, imp$Feature)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
lvls = task$class_names
nlvls = length(lvls)
if (is.null(pv$objective)) {
pv$objective = if (nlvls == 2L) "binary:logistic" else "multi:softprob"
}
if (self$predict_type == "prob" && pv$objective == "multi:softmax") {
stop("objective = 'multi:softmax' does not work with predict_type = 'prob'")
}
switch(pv$objective,
"multi:softprob" =,
"multi:softmax" = {
# add the number of classes 'num_class'
pv$num_class = nlvls
# we have to set this to avoid a deprecation warning
pv$eval_metric = pv$eval_metric %??% "mlogloss"
},
"binary:logistic" = {
pv$eval_metric = pv$eval_metric %??% "logloss"
}
)
data = task$data(cols = task$feature_names)
# recode to 0:1 to that for the binary case the positive class translates to 1 (#32)
# note that task$truth() is guaranteed to have the factor levels in the right order
label = nlvls - as.integer(task$truth())
data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = label)
if ("weights" %in% task$properties) {
xgboost::setinfo(data, "weight", task$weights$weight)
}
if (pv$early_stopping_set != "none") {
pv$watchlist = c(pv$watchlist, list(train = data))
}
# the last element in the watchlist is used as the early stopping set
if (pv$early_stopping_set == "test" && !is.null(task$row_roles$test)) {
test_data = task$data(rows = task$row_roles$test, cols = task$feature_names)
test_label = nlvls - as.integer(task$truth(rows = task$row_roles$test))
test_data = xgboost::xgb.DMatrix(data = as_numeric_matrix(test_data), label = test_label)
pv$watchlist = c(pv$watchlist, list(test = test_data))
}
pv$early_stopping_set = NULL
invoke(xgboost::xgb.train, data = data, .args = pv)
},
.predict = function(task) {
pv = self$param_set$get_values(tags = "predict")
model = self$model
response = prob = NULL
lvls = rev(task$class_names)
nlvls = length(lvls)
if (is.null(pv$objective)) {
pv$objective = ifelse(nlvls == 2L, "binary:logistic", "multi:softprob")
}
newdata = as_numeric_matrix(ordered_features(task, self))
pred = invoke(predict, model, newdata = newdata, .args = pv)
if (nlvls == 2L) { # binaryclass
if (pv$objective == "multi:softprob") {
prob = matrix(pred, ncol = nlvls, byrow = TRUE)
colnames(prob) = lvls
} else {
prob = pvec2mat(pred, lvls)
}
} else { # multiclass
if (pv$objective == "multi:softmax") {
response = lvls[pred + 1L]
} else {
prob = matrix(pred, ncol = nlvls, byrow = TRUE)
colnames(prob) = lvls
}
}
if (!is.null(response)) {
list(response = response)
} else if (self$predict_type == "response") {
i = max.col(prob, ties.method = "random")
list(response = factor(colnames(prob)[i], levels = lvls))
} else {
list(prob = prob)
}
},
.hotstart = function(task) {
model = self$model
pars = self$param_set$get_values(tags = "train")
pars_train = self$state$param_vals
if (!is.null(pars_train$early_stopping_rounds)) {
stop("The parameter `early_stopping_rounds` is set. Early stopping and hotstarting are incompatible.")
}
pars$early_stopping_set = NULL
# Calculate additional boosting iterations
# niter in model and nrounds in ps should be equal after train and continue
pars$nrounds = pars$nrounds - pars_train$nrounds
# Construct data
nlvls = length(task$class_names)
data = task$data(cols = task$feature_names)
label = nlvls - as.integer(task$truth())
data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = label)
invoke(xgboost::xgb.train, data = data, xgb_model = model, .args = pars)
}
)
)
#' @include aaa.R
learners[["classif.xgboost"]] = LearnerClassifXgboost
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#' @title Extreme Gradient Boosting Classification Learner
#'
#' @name mlr_learners_classif.xgboost
#'
#' @description
#' eXtreme Gradient Boosting classification.
#' Calls [xgboost::xgb.train()] from package \CRANpkg{xgboost}.
#'
#' If not specified otherwise, the evaluation metric is set to the default `"logloss"`
#' for binary classification problems and set to `"mlogloss"` for multiclass problems.
#' This was necessary to silence a deprecation warning.
#'
#' Note that using the `watchlist` parameter directly will lead to problems when wrapping this [`Learner`] in a
#' `mlr3pipelines` `GraphLearner` as the preprocessing steps will not be applied to the data in the watchlist.
#'
#' @template note_xgboost
#' @section Initial parameter values:
#' - `nrounds`:
#' - Actual default: no default.
#' - Adjusted default: 1.
#' - Reason for change: Without a default construction of the learner
#' would error. Just setting a nonsense default to workaround this.
#' `nrounds` needs to be tuned by the user.
#' - `nthread`:
#' - Actual value: Undefined, triggering auto-detection of the number of CPUs.
#' - Adjusted value: 1.
#' - Reason for change: Conflicting with parallelization via \CRANpkg{future}.
#' - `verbose`:
#' - Actual default: 1.
#' - Adjusted default: 0.
#' - Reason for change: Reduce verbosity.
#'
#' @section Early stopping:
#' Early stopping can be used to find the optimal number of boosting rounds.
#' The `early_stopping_set` parameter controls which set is used to monitor the performance.
#' Set `early_stopping_set = "test"` to monitor the performance of the model on the test set while training.
#' The test set for early stopping can be set with the `"test"` row role in the [mlr3::Task].
#' Additionally, the range must be set in which the performance must increase with `early_stopping_rounds` and the maximum number of boosting rounds with `nrounds`.
#' While resampling, the test set is automatically applied from the [mlr3::Resampling].
#' Not that using the test set for early stopping can potentially bias the performance scores.
#' See the section on early stopping in the examples.
#'
#' @templateVar id classif.xgboost
#' @template learner
#'
#' @references
#' `r format_bib("chen_2016")`
#'
#' @export
#' @template seealso_learner
#' @template example_dontrun
#' @examples
#'
#' \dontrun{
#' # Train learner with early stopping on spam data set
#' task = tsk("spam")
#'
#' # Split task into training and test set
#' split = partition(task, ratio = 0.8)
#' task$set_row_roles(split$test, "test")
#'
#' # Set early stopping parameter
#' learner = lrn("classif.xgboost",
#' nrounds = 100,
#' early_stopping_rounds = 10,
#' early_stopping_set = "test"
#' )
#'
#' # Train learner with early stopping
#' learner$train(task)
#' }
LearnerClassifXgboost = R6Class("LearnerClassifXgboost",
inherit = LearnerClassif,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
alpha = p_dbl(0, default = 0, tags = "train"),
approxcontrib = p_lgl(default = FALSE, tags = "predict"),
base_score = p_dbl(default = 0.5, tags = "train"),
booster = p_fct(c("gbtree", "gblinear", "dart"), default = "gbtree", tags = c("train", "control")),
callbacks = p_uty(default = list(), tags = "train"),
colsample_bylevel = p_dbl(0, 1, default = 1, tags = "train"),
colsample_bynode = p_dbl(0, 1, default = 1, tags = "train"),
colsample_bytree = p_dbl(0, 1, default = 1, tags = c("train", "control")),
device = p_uty(default = "cpu", tags = "train"),
disable_default_eval_metric = p_lgl(default = FALSE, tags = "train"),
early_stopping_rounds = p_int(1L, default = NULL, special_vals = list(NULL), tags = "train"),
early_stopping_set = p_fct(c("none", "train", "test"), default = "none", tags = "train"),
eta = p_dbl(0, 1, default = 0.3, tags = c("train", "control")),
eval_metric = p_uty(tags = "train"),
feature_selector = p_fct(c("cyclic", "shuffle", "random", "greedy", "thrifty"), default = "cyclic", tags = "train"),
feval = p_uty(default = NULL, tags = "train"),
gamma = p_dbl(0, default = 0, tags = c("train", "control")),
grow_policy = p_fct(c("depthwise", "lossguide"), default = "depthwise", tags = "train"),
interaction_constraints = p_uty(tags = "train"),
iterationrange = p_uty(tags = "predict"),
lambda = p_dbl(0, default = 1, tags = "train"),
lambda_bias = p_dbl(0, default = 0, tags = "train"),
max_bin = p_int(2L, default = 256L, tags = "train"),
max_delta_step = p_dbl(0, default = 0, tags = "train"),
max_depth = p_int(0L, default = 6L, tags = c("train", "control")),
max_leaves = p_int(0L, default = 0L, tags = "train"),
maximize = p_lgl(default = NULL, special_vals = list(NULL), tags = "train"),
min_child_weight = p_dbl(0, default = 1, tags = c("train", "control")),
missing = p_dbl(default = NA, tags = c("train", "predict"), special_vals = list(NA, NA_real_, NULL)),
monotone_constraints = p_uty(default = 0, tags = c("train", "control"), custom_check = crate(function(x) { checkmate::check_integerish(x, lower = -1, upper = 1, any.missing = FALSE) })), # nolint
normalize_type = p_fct(c("tree", "forest"), default = "tree", tags = "train"),
nrounds = p_int(1L, tags = c("train", "hotstart")),
nthread = p_int(1L, default = 1L, tags = c("train", "control", "threads")),
ntreelimit = p_int(1L, default = NULL, special_vals = list(NULL), tags = "predict"),
num_parallel_tree = p_int(1L, default = 1L, tags = c("train", "control")),
objective = p_uty(default = "binary:logistic", tags = c("train", "predict", "control")),
one_drop = p_lgl(default = FALSE, tags = "train"),
outputmargin = p_lgl(default = FALSE, tags = "predict"),
predcontrib = p_lgl(default = FALSE, tags = "predict"),
predinteraction = p_lgl(default = FALSE, tags = "predict"),
predleaf = p_lgl(default = FALSE, tags = "predict"),
print_every_n = p_int(1L, default = 1L, tags = "train"),
process_type = p_fct(c("default", "update"), default = "default", tags = "train"),
rate_drop = p_dbl(0, 1, default = 0, tags = "train"),
refresh_leaf = p_lgl(default = TRUE, tags = "train"),
reshape = p_lgl(default = FALSE, tags = "predict"),
seed_per_iteration = p_lgl(default = FALSE, tags = "train"),
sampling_method = p_fct(c("uniform", "gradient_based"), default = "uniform", tags = "train"),
sample_type = p_fct(c("uniform", "weighted"), default = "uniform", tags = "train"),
save_name = p_uty(default = NULL, tags = "train"),
save_period = p_int(0, default = NULL, special_vals = list(NULL), tags = "train"),
scale_pos_weight = p_dbl(default = 1, tags = "train"),
skip_drop = p_dbl(0, 1, default = 0, tags = "train"),
strict_shape = p_lgl(default = FALSE, tags = "predict"),
subsample = p_dbl(0, 1, default = 1, tags = c("train", "control")),
top_k = p_int(0, default = 0, tags = "train"),
training = p_lgl(default = FALSE, tags = "predict"),
tree_method = p_fct(c("auto", "exact", "approx", "hist", "gpu_hist"), default = "auto", tags = "train"),
tweedie_variance_power = p_dbl(1, 2, default = 1.5, tags = "train"),
updater = p_uty(tags = "train"), # Default depends on the selected booster
verbose = p_int(0L, 2L, default = 1L, tags = "train"),
watchlist = p_uty(default = NULL, tags = "train"),
xgb_model = p_uty(default = NULL, tags = "train")
)
# param deps
ps$add_dep("tweedie_variance_power", "objective", CondEqual$new("reg:tweedie"))
ps$add_dep("print_every_n", "verbose", CondEqual$new(1L))
ps$add_dep("sampling_method", "booster", CondEqual$new("gbtree"))
ps$add_dep("normalize_type", "booster", CondEqual$new("dart"))
ps$add_dep("rate_drop", "booster", CondEqual$new("dart"))
ps$add_dep("skip_drop", "booster", CondEqual$new("dart"))
ps$add_dep("one_drop", "booster", CondEqual$new("dart"))
ps$add_dep("sample_type", "booster", CondEqual$new("dart"))
ps$add_dep("tree_method", "booster", CondAnyOf$new(c("gbtree", "dart")))
ps$add_dep("grow_policy", "tree_method", CondEqual$new("hist"))
ps$add_dep("max_leaves", "grow_policy", CondEqual$new("lossguide"))
ps$add_dep("max_bin", "tree_method", CondEqual$new("hist"))
ps$add_dep("feature_selector", "booster", CondEqual$new("gblinear"))
ps$add_dep("top_k", "booster", CondEqual$new("gblinear"))
ps$add_dep("top_k", "feature_selector", CondAnyOf$new(c("greedy", "thrifty")))
ps$add_dep("lambda_bias", "booster", CondEqual$new("gblinear"))
# custom defaults
ps$values = list(nrounds = 1L, nthread = 1L, verbose = 0L, early_stopping_set = "none")
super$initialize(
id = "classif.xgboost",
predict_types = c("response", "prob"),
param_set = ps,
feature_types = c("logical", "integer", "numeric"),
properties = c("weights", "missings", "twoclass", "multiclass", "importance", "hotstart_forward"),
packages = c("mlr3learners", "xgboost"),
label = "Extreme Gradient Boosting",
man = "mlr3learners::mlr_learners_classif.xgboost"
)
},
#' @description
#' The importance scores are calculated with [xgboost::xgb.importance()].
#'
#' @return Named `numeric()`.
importance = function() {
if (is.null(self$model)) {
stopf("No model stored")
}
imp = xgboost::xgb.importance(
model = self$model
)
set_names(imp$Gain, imp$Feature)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
lvls = task$class_names
nlvls = length(lvls)
if (is.null(pv$objective)) {
pv$objective = if (nlvls == 2L) "binary:logistic" else "multi:softprob"
}
if (self$predict_type == "prob" && pv$objective == "multi:softmax") {
stop("objective = 'multi:softmax' does not work with predict_type = 'prob'")
}
switch(pv$objective,
"multi:softprob" =,
"multi:softmax" = {
# add the number of classes 'num_class'
pv$num_class = nlvls
# we have to set this to avoid a deprecation warning
pv$eval_metric = pv$eval_metric %??% "mlogloss"
},
"binary:logistic" = {
pv$eval_metric = pv$eval_metric %??% "logloss"
}
)
data = task$data(cols = task$feature_names)
# recode to 0:1 to that for the binary case the positive class translates to 1 (#32)
# note that task$truth() is guaranteed to have the factor levels in the right order
label = nlvls - as.integer(task$truth())
data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = label)
if ("weights" %in% task$properties) {
xgboost::setinfo(data, "weight", task$weights$weight)
}
if (pv$early_stopping_set != "none") {
pv$watchlist = c(pv$watchlist, list(train = data))
}
# the last element in the watchlist is used as the early stopping set
if (pv$early_stopping_set == "test" && !is.null(task$row_roles$test)) {
test_data = task$data(rows = task$row_roles$test, cols = task$feature_names)
test_label = nlvls - as.integer(task$truth(rows = task$row_roles$test))
test_data = xgboost::xgb.DMatrix(data = as_numeric_matrix(test_data), label = test_label)
pv$watchlist = c(pv$watchlist, list(test = test_data))
}
pv$early_stopping_set = NULL
invoke(xgboost::xgb.train, data = data, .args = pv)
},
.predict = function(task) {
pv = self$param_set$get_values(tags = "predict")
model = self$model
response = prob = NULL
lvls = rev(task$class_names)
nlvls = length(lvls)
if (is.null(pv$objective)) {
pv$objective = ifelse(nlvls == 2L, "binary:logistic", "multi:softprob")
}
newdata = as_numeric_matrix(ordered_features(task, self))
pred = invoke(predict, model, newdata = newdata, .args = pv)
if (nlvls == 2L) { # binaryclass
if (pv$objective == "multi:softprob") {
prob = matrix(pred, ncol = nlvls, byrow = TRUE)
colnames(prob) = lvls
} else {
prob = pvec2mat(pred, lvls)
}
} else { # multiclass
if (pv$objective == "multi:softmax") {
response = lvls[pred + 1L]
} else {
prob = matrix(pred, ncol = nlvls, byrow = TRUE)
colnames(prob) = lvls
}
}
if (!is.null(response)) {
list(response = response)
} else if (self$predict_type == "response") {
i = max.col(prob, ties.method = "random")
list(response = factor(colnames(prob)[i], levels = lvls))
} else {
list(prob = prob)
}
},
.hotstart = function(task) {
model = self$model
pars = self$param_set$get_values(tags = "train")
pars_train = self$state$param_vals
if (!is.null(pars_train$early_stopping_rounds)) {
stop("The parameter `early_stopping_rounds` is set. Early stopping and hotstarting are incompatible.")
}
pars$early_stopping_set = NULL
# Calculate additional boosting iterations
# niter in model and nrounds in ps should be equal after train and continue
pars$nrounds = pars$nrounds - pars_train$nrounds
# Construct data
nlvls = length(task$class_names)
data = task$data(cols = task$feature_names)
label = nlvls - as.integer(task$truth())
data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = label)
invoke(xgboost::xgb.train, data = data, xgb_model = model, .args = pars)
}
)
)
#' @include aaa.R
learners[["classif.xgboost"]] = LearnerClassifXgboost
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