R/AutoTuner.R
In mlr-org/mlr3tuning: Hyperparameter Optimization for 'mlr3'
Defines functions
set_validate.AutoTuner
unmarshal_model.auto_tuner_model_marshaled
marshal_model.auto_tuner_model
Documented in
set_validate.AutoTuner
#' @title Class for Automatic Tuning
#'
#' @description
#'
#' The [AutoTuner] wraps a [mlr3::Learner] and augments it with an automatic tuning process for a given set of hyperparameters.
#' The [auto_tuner()] function creates an [AutoTuner] object.
#'
#' @section Validation:
#' The `AutoTuner` itself does **not** have the `"validation"` property.
#' To enable validation during the tuning, set the `$validate` field of the tuned learner.
#' This is also possible via `set_validate()`.
#'
#' @details
#' The [AutoTuner] is a [mlr3::Learner] which wraps another [mlr3::Learner] and performs the following steps during `$train()`:
#'
#' 1. The hyperparameters of the wrapped (inner) learner are trained on the training data via resampling.
#' The tuning can be specified by providing a [Tuner], a [bbotk::Terminator], a search space as [paradox::ParamSet], a [mlr3::Resampling] and a [mlr3::Measure].
#' 2. The best found hyperparameter configuration is set as hyperparameters for the wrapped (inner) learner stored in `at$learner`.
#' Access the tuned hyperparameters via `at$tuning_result`.
#' 3. A final model is fit on the complete training data using the now parametrized wrapped learner.
#' The respective model is available via field `at$learner$model`.
#'
#' During `$predict()` the `AutoTuner` just calls the predict method of the wrapped (inner) learner.
#' A set timeout is disabled while fitting the final model.
#'
#' @inheritSection TuningInstanceBatchSingleCrit Default Measures
#' @inheritSection TuningInstanceBatchSingleCrit Resources
#'
#' @section Nested Resampling:
#' Nested resampling is performed by passing an [AutoTuner] to [mlr3::resample()] or [mlr3::benchmark()].
#' To access the inner resampling results, set `store_tuning_instance = TRUE` and execute [mlr3::resample()] or [mlr3::benchmark()] with `store_models = TRUE` (see examples).
#' The [mlr3::Resampling] passed to the [AutoTuner] is meant to be the inner resampling, operating on the training set of an arbitrary outer resampling.
#' For this reason, the inner resampling should be not instantiated.
#' If an instantiated resampling is passed, the [AutoTuner] fails when a row id of the inner resampling is not present in the training set of the outer resampling.
#'
#'
#' @template param_learner
#' @template param_resampling
#' @template param_measure
#' @template param_terminator
#' @template param_search_space
#' @template param_store_tuning_instance
#' @template param_store_benchmark_result
#' @template param_store_models
#' @template param_check_values
#' @template param_callbacks
#' @template param_rush
#' @template param_id
#'
#' @export
#' @examples
#' # Automatic Tuning
#'
#' # split to train and external set
#' task = tsk("penguins")
#' split = partition(task, ratio = 0.8)
#'
#' # load learner and set search space
#' learner = lrn("classif.rpart",
#' cp = to_tune(1e-04, 1e-1, logscale = TRUE)
#' )
#'
#' # create auto tuner
#' at = auto_tuner(
#' tuner = tnr("random_search"),
#' learner = learner,
#' resampling = rsmp ("holdout"),
#' measure = msr("classif.ce"),
#' term_evals = 4)
#'
#' # tune hyperparameters and fit final model
#' at$train(task, row_ids = split$train)
#'
#' # predict with final model
#' at$predict(task, row_ids = split$test)
#'
#' # show tuning result
#' at$tuning_result
#'
#' # model slot contains trained learner and tuning instance
#' at$model
#'
#' # shortcut trained learner
#' at$learner
#'
#' # shortcut tuning instance
#' at$tuning_instance
#'
#'
#' # Nested Resampling
#'
#' at = auto_tuner(
#' tuner = tnr("random_search"),
#' learner = learner,
#' resampling = rsmp ("holdout"),
#' measure = msr("classif.ce"),
#' term_evals = 4)
#'
#' resampling_outer = rsmp("cv", folds = 3)
#' rr = resample(task, at, resampling_outer, store_models = TRUE)
#'
#' # retrieve inner tuning results.
#' extract_inner_tuning_results(rr)
#'
#' # performance scores estimated on the outer resampling
#' rr$score()
#'
#' # unbiased performance of the final model trained on the full data set
#' rr$aggregate()
AutoTuner = R6Class("AutoTuner",
inherit = Learner,
public = list(
#' @field instance_args (`list()`)\cr
#' All arguments from construction to create the [TuningInstanceBatchSingleCrit].
instance_args = NULL,
#' @field tuner ([Tuner])\cr
#' Optimization algorithm.
tuner = NULL,
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param tuner ([Tuner])\cr
#' Optimization algorithm.
initialize = function(
tuner,
learner,
resampling,
measure = NULL,
terminator,
search_space = NULL,
store_tuning_instance = TRUE,
store_benchmark_result = TRUE,
store_models = FALSE,
check_values = FALSE,
callbacks = NULL,
rush = NULL,
id = NULL
) {
learner = assert_learner(as_learner(learner, clone = TRUE))
if (!is.null(search_space) && length(learner$param_set$get_values(type = "only_token", check_required = FALSE)) > 0) {
stop("If the values of the ParamSet of the Learner contain TuneTokens you cannot supply a search_space.")
}
ia = list()
self$tuner = assert_tuner(tuner)
ia$learner = learner
ia$resampling = assert_resampling(resampling)$clone()
if (!is.null(measure)) ia$measure = assert_measure(as_measure(measure), learner = learner)
if (!is.null(search_space)) ia$search_space = search_space
ia$terminator = assert_terminator(terminator)$clone()
ia$store_models = assert_flag(store_models)
ia$store_benchmark_result = assert_flag(store_benchmark_result) || ia$store_models
private$.store_tuning_instance = assert_flag(store_tuning_instance) || ia$store_benchmark_result
ia$check_values = assert_flag(check_values)
ia$callbacks = assert_callbacks(as_callbacks(callbacks))
if (!is.null(rush)) ia$rush = assert_class(rush, "Rush")
self$instance_args = ia
id = assert_string(id, null.ok = TRUE) %??% paste0(learner$id, ".tuned")
super$initialize(
id = id,
task_type = learner$task_type,
packages = c("mlr3tuning", learner$packages),
feature_types = learner$feature_types,
predict_types = learner$predict_types,
properties = setdiff(learner$properties, c("internal_tuning", "validation"))
)
self$predict_type = learner$predict_type
self$predict_sets = learner$predict_sets
},
#' @description
#' Extracts the base learner from nested learner objects like `GraphLearner` in \CRANpkg{mlr3pipelines}.
#' If `recursive = 0`, the (tuned) learner is returned.
#'
#' @param recursive (`integer(1)`)\cr
#' Depth of recursion for multiple nested objects.
#'
#' @return [mlr3::Learner].
base_learner = function(recursive = Inf) {
if (recursive == 0L) self$learner else self$learner$base_learner(recursive - 1L)
},
#' @description
#' The importance scores of the final model.
#'
#' @return Named `numeric()`.
importance = function() {
if ("importance" %nin% self$instance_args$learner$properties) {
stopf("Learner ''%s' cannot calculate important scores.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$importance()
} else {
self$model$learner$importance()
}
},
#' @description
#' The selected features of the final model.
#'
#' @return `character()`.
selected_features = function() {
if ("selected_features" %nin% self$instance_args$learner$properties) {
stopf("Learner ''%s' cannot select features.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$selected_features()
} else {
self$model$learner$selected_features()
}
},
#' @description
#' The out-of-bag error of the final model.
#'
#' @return `numeric(1)`.
oob_error = function() {
if ("oob_error" %nin% self$instance_args$learner$properties) {
stopf("Learner '%s' cannot calculate the out-of-bag error.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$oob_error()
} else {
self$model$learner$oob_error()
}
},
#' @description
#' The log-likelihood of the final model.
#'
#' @return `logLik`.
loglik = function() {
if ("loglik" %nin% self$instance_args$learner$properties) {
stopf("Learner '%s' cannot calculate the log-likelihood.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$loglik()
} else {
self$model$learner$loglik()
}
},
#' Printer.
#' @param ... (ignored).
print = function() {
super$print()
search_space = if (is.null(self$instance_args$search_space)) {
self$instance_args$learner$param_set$search_space()
} else {
self$instance_args$search_space
}
catf("* Search Space:")
print(as.data.table(search_space)[, c("id", "class", "lower", "upper", "nlevels"), with = FALSE])
},
#' @description
#' Marshal the learner.
#' @param ... (any)\cr
#' Additional parameters.
#' @return self
marshal = function(...) {
learner_marshal(.learner = self, ...)
},
#' @description
#' Unmarshal the learner.
#' @param ... (any)\cr
#' Additional parameters.
#' @return self
unmarshal = function(...) {
learner_unmarshal(.learner = self, ...)
},
#' @description
#' Whether the learner is marshaled.
marshaled = function() {
learner_marshaled(self)
}
),
active = list(
#' @field archive [ArchiveBatchTuning]\cr
#' Archive of the [TuningInstanceBatchSingleCrit].
archive = function() self$tuning_instance$archive,
#' @field learner ([mlr3::Learner])\cr
#' Trained learner
learner = function() {
# if there is no trained learner, we return the one in instance args
if (is.null(self$model$learner$model)) {
self$instance_args$learner
} else {
self$model$learner
}
},
#' @field tuning_instance ([TuningInstanceAsyncSingleCrit] | [TuningInstanceBatchSingleCrit])\cr
#' Internally created tuning instance with all intermediate results.
tuning_instance = function() self$model$tuning_instance,
#' @field tuning_result ([data.table::data.table])\cr
#' Short-cut to `result` from tuning instance.
tuning_result = function() self$tuning_instance$result,
#' @field predict_type (`character(1)`)\cr
#' Stores the currently active predict type, e.g. `"response"`.
#' Must be an element of `$predict_types`.
#' A few learners already use the predict type during training.
#' So there is no guarantee that changing the predict type after tuning and training will have any effect or does not lead to errors.
predict_type = function(rhs) {
if (missing(rhs)) {
return(private$.predict_type)
}
if (rhs %nin% self$predict_types) {
stopf("Learner '%s' does not support predict type '%s'", self$id, rhs)
}
self$instance_args$learner$predict_type = rhs
if (!is.null(self$model)) {
self$model$learner$predict_type = rhs
}
private$.predict_type = rhs
},
#' @field hash (`character(1)`)\cr
#' Hash (unique identifier) for this object.
hash = function(rhs) {
assert_ro_binding(rhs)
calculate_hash(class(self), self$id, self$param_set$values, private$.predict_type, self$fallback$hash, self$parallel_predict, self$tuner, self$instance_args, private$.store_tuning_instance)
},
#' @field phash (`character(1)`)\cr
#' Hash (unique identifier) for this partial object, excluding some components which are varied systematically during tuning (parameter values) or feature selection (feature names).
phash = function(rhs) {
assert_ro_binding(rhs)
self$hash
}
),
private = list(
.train = function(task) {
# construct instance from args; then tune
ia = self$instance_args
ia$task = task
learner = ia$learner$clone(deep = TRUE)
# check if task contains all row ids required for instantiated resampling
if (ia$resampling$is_instantiated) {
imap(ia$resampling$instance$train, function(x, i) {
if (!test_subset(x, task$row_ids)) {
stopf("Train set %i of inner resampling '%s' contains row ids not present in task '%s': {%s}", i, ia$resampling$id, task$id, paste(setdiff(x, task$row_ids), collapse = ", "))
}
})
imap(ia$resampling$instance$test, function(x, i) {
if (!test_subset(x, task$row_ids)) {
stopf("Test set %i of inner resampling '%s' contains row ids not present in task '%s': {%s}", i, ia$resampling$id, task$id, paste(setdiff(x, task$row_ids), collapse = ", "))
}
})
}
TuningInstance = if (inherits(self$tuner, "TunerBatch")) TuningInstanceBatchSingleCrit else TuningInstanceAsyncSingleCrit
instance = do.call(TuningInstance$new, ia)
self$tuner$optimize(instance)
# now we reset the validation task and clear the exit handlers
# in the case of internal tuning, the result_learner_param_vals already contains the aggregated internally
learner$param_set$values = instance$result_learner_param_vals
# disable timeout to allow train on full data set without time limit
# timeout during tuning is not affected
learner$timeout = c(train = Inf, predict = Inf)
if ("validation" %in% learner$properties) {
set_validate(learner, validate = NULL)
}
learner$train(task)
# the return model is a list of "learner" and "tuning_instance"
result_model = list(learner = learner)
if (private$.store_tuning_instance) result_model$tuning_instance = instance
structure(result_model, class = c("auto_tuner_model", "list"))
},
.predict = function(task) {
self$model$learner$predict(task)
},
.extract_internal_valid_scores = function() {
self$model$learner$internal_valid_scores
},
.store_tuning_instance = NULL
)
)
#' @export
marshal_model.auto_tuner_model = function(model, inplace = FALSE, ...) {
if (inplace) {
model$learner$model = marshal_model(model$learner$model, inplace = TRUE)
x = structure(list(
marshaled = model,
packages = "mlr3tuning"
), class = c("auto_tuner_model_marshaled", "list_marshaled", "marshaled"))
return(x)
}
# we clone the learner without its model
learner = model$learner
learner_model = learner$model
on.exit({learner$model = learner_model}, add = TRUE)
learner$model = NULL
learner_clone = learner$clone(deep = TRUE)
learner_clone$model = marshal_model(learner_model, inplace = FALSE)
marshaled = list(learner = learner_clone)
# note that we don't clone the tuning instance even when inplace is FALSE
# For our use-case, this is not necessary and would cause unnecessary overhead in the the mlr3 workhorse function
marshaled$tuning_instance = model$tuning_instance
structure(list(
marshaled = marshaled
), class = c("auto_tuner_model_marshaled", "list_marshaled", "marshaled"))
}
#' @export
unmarshal_model.auto_tuner_model_marshaled = function(model, inplace = FALSE, ...) {
if (inplace) {
at_model = model$marshaled
at_model$learner$model = unmarshal_model(at_model$learner$model, inplace = TRUE)
return(at_model)
}
at_model = model$marshaled
prev_learner = at_model$learner
prev_model = prev_learner$model
prev_learner$model = NULL
on.exit({prev_learner$model = prev_model}, add = TRUE)
at_model$learner = prev_learner$clone(deep = TRUE)
at_model$learner$model = unmarshal_model(prev_model, inplace = FALSE)
return(structure(at_model, class = c("auto_tuner_model", "list")))
}
#' @title Configure Validation for AutoTuner
#'
#' @description
#' Configure validation data for the learner that is tuned by the `AutoTuner`.
#'
#' @param learner ([`AutoTuner`])\cr
#' The autotuner for which to enable validation.
#' @param validate (`numeric(1)`, `"predefined"`, `"test"`, or `NULL`)\cr
#' How to configure the validation during the hyperparameter tuning.
#' @param ... (any)\cr
#' Passed when calling `set_validate()` on the wrapped leaerner.
#' @export
#' @examples
#' at = auto_tuner(
#' tuner = tnr("random_search"),
#' learner = lrn("classif.debug", early_stopping = TRUE,
#' iter = to_tune(upper = 1000L, internal = TRUE), validate = 0.2),
#' resampling = rsmp("holdout")
#' )
#' # use the test set as validation data during tuning
#' set_validate(at, validate = "test")
#' at$learner$validate
set_validate.AutoTuner = function(learner, validate, ...) {
set_validate(learner$learner, validate = validate, ...)
invisible(learner)
}
mlr-org/mlr3tuning documentation built on April 14, 2025, 1 a.m.
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Defines functions set_validate.AutoTuner unmarshal_model.auto_tuner_model_marshaled marshal_model.auto_tuner_model
Documented in set_validate.AutoTuner
#' @title Class for Automatic Tuning
#'
#' @description
#'
#' The [AutoTuner] wraps a [mlr3::Learner] and augments it with an automatic tuning process for a given set of hyperparameters.
#' The [auto_tuner()] function creates an [AutoTuner] object.
#'
#' @section Validation:
#' The `AutoTuner` itself does **not** have the `"validation"` property.
#' To enable validation during the tuning, set the `$validate` field of the tuned learner.
#' This is also possible via `set_validate()`.
#'
#' @details
#' The [AutoTuner] is a [mlr3::Learner] which wraps another [mlr3::Learner] and performs the following steps during `$train()`:
#'
#' 1. The hyperparameters of the wrapped (inner) learner are trained on the training data via resampling.
#' The tuning can be specified by providing a [Tuner], a [bbotk::Terminator], a search space as [paradox::ParamSet], a [mlr3::Resampling] and a [mlr3::Measure].
#' 2. The best found hyperparameter configuration is set as hyperparameters for the wrapped (inner) learner stored in `at$learner`.
#' Access the tuned hyperparameters via `at$tuning_result`.
#' 3. A final model is fit on the complete training data using the now parametrized wrapped learner.
#' The respective model is available via field `at$learner$model`.
#'
#' During `$predict()` the `AutoTuner` just calls the predict method of the wrapped (inner) learner.
#' A set timeout is disabled while fitting the final model.
#'
#' @inheritSection TuningInstanceBatchSingleCrit Default Measures
#' @inheritSection TuningInstanceBatchSingleCrit Resources
#'
#' @section Nested Resampling:
#' Nested resampling is performed by passing an [AutoTuner] to [mlr3::resample()] or [mlr3::benchmark()].
#' To access the inner resampling results, set `store_tuning_instance = TRUE` and execute [mlr3::resample()] or [mlr3::benchmark()] with `store_models = TRUE` (see examples).
#' The [mlr3::Resampling] passed to the [AutoTuner] is meant to be the inner resampling, operating on the training set of an arbitrary outer resampling.
#' For this reason, the inner resampling should be not instantiated.
#' If an instantiated resampling is passed, the [AutoTuner] fails when a row id of the inner resampling is not present in the training set of the outer resampling.
#'
#'
#' @template param_learner
#' @template param_resampling
#' @template param_measure
#' @template param_terminator
#' @template param_search_space
#' @template param_store_tuning_instance
#' @template param_store_benchmark_result
#' @template param_store_models
#' @template param_check_values
#' @template param_callbacks
#' @template param_rush
#' @template param_id
#'
#' @export
#' @examples
#' # Automatic Tuning
#'
#' # split to train and external set
#' task = tsk("penguins")
#' split = partition(task, ratio = 0.8)
#'
#' # load learner and set search space
#' learner = lrn("classif.rpart",
#' cp = to_tune(1e-04, 1e-1, logscale = TRUE)
#' )
#'
#' # create auto tuner
#' at = auto_tuner(
#' tuner = tnr("random_search"),
#' learner = learner,
#' resampling = rsmp ("holdout"),
#' measure = msr("classif.ce"),
#' term_evals = 4)
#'
#' # tune hyperparameters and fit final model
#' at$train(task, row_ids = split$train)
#'
#' # predict with final model
#' at$predict(task, row_ids = split$test)
#'
#' # show tuning result
#' at$tuning_result
#'
#' # model slot contains trained learner and tuning instance
#' at$model
#'
#' # shortcut trained learner
#' at$learner
#'
#' # shortcut tuning instance
#' at$tuning_instance
#'
#'
#' # Nested Resampling
#'
#' at = auto_tuner(
#' tuner = tnr("random_search"),
#' learner = learner,
#' resampling = rsmp ("holdout"),
#' measure = msr("classif.ce"),
#' term_evals = 4)
#'
#' resampling_outer = rsmp("cv", folds = 3)
#' rr = resample(task, at, resampling_outer, store_models = TRUE)
#'
#' # retrieve inner tuning results.
#' extract_inner_tuning_results(rr)
#'
#' # performance scores estimated on the outer resampling
#' rr$score()
#'
#' # unbiased performance of the final model trained on the full data set
#' rr$aggregate()
AutoTuner = R6Class("AutoTuner",
inherit = Learner,
public = list(
#' @field instance_args (`list()`)\cr
#' All arguments from construction to create the [TuningInstanceBatchSingleCrit].
instance_args = NULL,
#' @field tuner ([Tuner])\cr
#' Optimization algorithm.
tuner = NULL,
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param tuner ([Tuner])\cr
#' Optimization algorithm.
initialize = function(
tuner,
learner,
resampling,
measure = NULL,
terminator,
search_space = NULL,
store_tuning_instance = TRUE,
store_benchmark_result = TRUE,
store_models = FALSE,
check_values = FALSE,
callbacks = NULL,
rush = NULL,
id = NULL
) {
learner = assert_learner(as_learner(learner, clone = TRUE))
if (!is.null(search_space) && length(learner$param_set$get_values(type = "only_token", check_required = FALSE)) > 0) {
stop("If the values of the ParamSet of the Learner contain TuneTokens you cannot supply a search_space.")
}
ia = list()
self$tuner = assert_tuner(tuner)
ia$learner = learner
ia$resampling = assert_resampling(resampling)$clone()
if (!is.null(measure)) ia$measure = assert_measure(as_measure(measure), learner = learner)
if (!is.null(search_space)) ia$search_space = search_space
ia$terminator = assert_terminator(terminator)$clone()
ia$store_models = assert_flag(store_models)
ia$store_benchmark_result = assert_flag(store_benchmark_result) || ia$store_models
private$.store_tuning_instance = assert_flag(store_tuning_instance) || ia$store_benchmark_result
ia$check_values = assert_flag(check_values)
ia$callbacks = assert_callbacks(as_callbacks(callbacks))
if (!is.null(rush)) ia$rush = assert_class(rush, "Rush")
self$instance_args = ia
id = assert_string(id, null.ok = TRUE) %??% paste0(learner$id, ".tuned")
super$initialize(
id = id,
task_type = learner$task_type,
packages = c("mlr3tuning", learner$packages),
feature_types = learner$feature_types,
predict_types = learner$predict_types,
properties = setdiff(learner$properties, c("internal_tuning", "validation"))
)
self$predict_type = learner$predict_type
self$predict_sets = learner$predict_sets
},
#' @description
#' Extracts the base learner from nested learner objects like `GraphLearner` in \CRANpkg{mlr3pipelines}.
#' If `recursive = 0`, the (tuned) learner is returned.
#'
#' @param recursive (`integer(1)`)\cr
#' Depth of recursion for multiple nested objects.
#'
#' @return [mlr3::Learner].
base_learner = function(recursive = Inf) {
if (recursive == 0L) self$learner else self$learner$base_learner(recursive - 1L)
},
#' @description
#' The importance scores of the final model.
#'
#' @return Named `numeric()`.
importance = function() {
if ("importance" %nin% self$instance_args$learner$properties) {
stopf("Learner ''%s' cannot calculate important scores.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$importance()
} else {
self$model$learner$importance()
}
},
#' @description
#' The selected features of the final model.
#'
#' @return `character()`.
selected_features = function() {
if ("selected_features" %nin% self$instance_args$learner$properties) {
stopf("Learner ''%s' cannot select features.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$selected_features()
} else {
self$model$learner$selected_features()
}
},
#' @description
#' The out-of-bag error of the final model.
#'
#' @return `numeric(1)`.
oob_error = function() {
if ("oob_error" %nin% self$instance_args$learner$properties) {
stopf("Learner '%s' cannot calculate the out-of-bag error.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$oob_error()
} else {
self$model$learner$oob_error()
}
},
#' @description
#' The log-likelihood of the final model.
#'
#' @return `logLik`.
loglik = function() {
if ("loglik" %nin% self$instance_args$learner$properties) {
stopf("Learner '%s' cannot calculate the log-likelihood.", self$instance_args$learner$id)
}
if (is.null(self$model$learner$model)) {
self$instance_args$learner$loglik()
} else {
self$model$learner$loglik()
}
},
#' Printer.
#' @param ... (ignored).
print = function() {
super$print()
search_space = if (is.null(self$instance_args$search_space)) {
self$instance_args$learner$param_set$search_space()
} else {
self$instance_args$search_space
}
catf("* Search Space:")
print(as.data.table(search_space)[, c("id", "class", "lower", "upper", "nlevels"), with = FALSE])
},
#' @description
#' Marshal the learner.
#' @param ... (any)\cr
#' Additional parameters.
#' @return self
marshal = function(...) {
learner_marshal(.learner = self, ...)
},
#' @description
#' Unmarshal the learner.
#' @param ... (any)\cr
#' Additional parameters.
#' @return self
unmarshal = function(...) {
learner_unmarshal(.learner = self, ...)
},
#' @description
#' Whether the learner is marshaled.
marshaled = function() {
learner_marshaled(self)
}
),
active = list(
#' @field archive [ArchiveBatchTuning]\cr
#' Archive of the [TuningInstanceBatchSingleCrit].
archive = function() self$tuning_instance$archive,
#' @field learner ([mlr3::Learner])\cr
#' Trained learner
learner = function() {
# if there is no trained learner, we return the one in instance args
if (is.null(self$model$learner$model)) {
self$instance_args$learner
} else {
self$model$learner
}
},
#' @field tuning_instance ([TuningInstanceAsyncSingleCrit] | [TuningInstanceBatchSingleCrit])\cr
#' Internally created tuning instance with all intermediate results.
tuning_instance = function() self$model$tuning_instance,
#' @field tuning_result ([data.table::data.table])\cr
#' Short-cut to `result` from tuning instance.
tuning_result = function() self$tuning_instance$result,
#' @field predict_type (`character(1)`)\cr
#' Stores the currently active predict type, e.g. `"response"`.
#' Must be an element of `$predict_types`.
#' A few learners already use the predict type during training.
#' So there is no guarantee that changing the predict type after tuning and training will have any effect or does not lead to errors.
predict_type = function(rhs) {
if (missing(rhs)) {
return(private$.predict_type)
}
if (rhs %nin% self$predict_types) {
stopf("Learner '%s' does not support predict type '%s'", self$id, rhs)
}
self$instance_args$learner$predict_type = rhs
if (!is.null(self$model)) {
self$model$learner$predict_type = rhs
}
private$.predict_type = rhs
},
#' @field hash (`character(1)`)\cr
#' Hash (unique identifier) for this object.
hash = function(rhs) {
assert_ro_binding(rhs)
calculate_hash(class(self), self$id, self$param_set$values, private$.predict_type, self$fallback$hash, self$parallel_predict, self$tuner, self$instance_args, private$.store_tuning_instance)
},
#' @field phash (`character(1)`)\cr
#' Hash (unique identifier) for this partial object, excluding some components which are varied systematically during tuning (parameter values) or feature selection (feature names).
phash = function(rhs) {
assert_ro_binding(rhs)
self$hash
}
),
private = list(
.train = function(task) {
# construct instance from args; then tune
ia = self$instance_args
ia$task = task
learner = ia$learner$clone(deep = TRUE)
# check if task contains all row ids required for instantiated resampling
if (ia$resampling$is_instantiated) {
imap(ia$resampling$instance$train, function(x, i) {
if (!test_subset(x, task$row_ids)) {
stopf("Train set %i of inner resampling '%s' contains row ids not present in task '%s': {%s}", i, ia$resampling$id, task$id, paste(setdiff(x, task$row_ids), collapse = ", "))
}
})
imap(ia$resampling$instance$test, function(x, i) {
if (!test_subset(x, task$row_ids)) {
stopf("Test set %i of inner resampling '%s' contains row ids not present in task '%s': {%s}", i, ia$resampling$id, task$id, paste(setdiff(x, task$row_ids), collapse = ", "))
}
})
}
TuningInstance = if (inherits(self$tuner, "TunerBatch")) TuningInstanceBatchSingleCrit else TuningInstanceAsyncSingleCrit
instance = do.call(TuningInstance$new, ia)
self$tuner$optimize(instance)
# now we reset the validation task and clear the exit handlers
# in the case of internal tuning, the result_learner_param_vals already contains the aggregated internally
learner$param_set$values = instance$result_learner_param_vals
# disable timeout to allow train on full data set without time limit
# timeout during tuning is not affected
learner$timeout = c(train = Inf, predict = Inf)
if ("validation" %in% learner$properties) {
set_validate(learner, validate = NULL)
}
learner$train(task)
# the return model is a list of "learner" and "tuning_instance"
result_model = list(learner = learner)
if (private$.store_tuning_instance) result_model$tuning_instance = instance
structure(result_model, class = c("auto_tuner_model", "list"))
},
.predict = function(task) {
self$model$learner$predict(task)
},
.extract_internal_valid_scores = function() {
self$model$learner$internal_valid_scores
},
.store_tuning_instance = NULL
)
)
#' @export
marshal_model.auto_tuner_model = function(model, inplace = FALSE, ...) {
if (inplace) {
model$learner$model = marshal_model(model$learner$model, inplace = TRUE)
x = structure(list(
marshaled = model,
packages = "mlr3tuning"
), class = c("auto_tuner_model_marshaled", "list_marshaled", "marshaled"))
return(x)
}
# we clone the learner without its model
learner = model$learner
learner_model = learner$model
on.exit({learner$model = learner_model}, add = TRUE)
learner$model = NULL
learner_clone = learner$clone(deep = TRUE)
learner_clone$model = marshal_model(learner_model, inplace = FALSE)
marshaled = list(learner = learner_clone)
# note that we don't clone the tuning instance even when inplace is FALSE
# For our use-case, this is not necessary and would cause unnecessary overhead in the the mlr3 workhorse function
marshaled$tuning_instance = model$tuning_instance
structure(list(
marshaled = marshaled
), class = c("auto_tuner_model_marshaled", "list_marshaled", "marshaled"))
}
#' @export
unmarshal_model.auto_tuner_model_marshaled = function(model, inplace = FALSE, ...) {
if (inplace) {
at_model = model$marshaled
at_model$learner$model = unmarshal_model(at_model$learner$model, inplace = TRUE)
return(at_model)
}
at_model = model$marshaled
prev_learner = at_model$learner
prev_model = prev_learner$model
prev_learner$model = NULL
on.exit({prev_learner$model = prev_model}, add = TRUE)
at_model$learner = prev_learner$clone(deep = TRUE)
at_model$learner$model = unmarshal_model(prev_model, inplace = FALSE)
return(structure(at_model, class = c("auto_tuner_model", "list")))
}
#' @title Configure Validation for AutoTuner
#'
#' @description
#' Configure validation data for the learner that is tuned by the `AutoTuner`.
#'
#' @param learner ([`AutoTuner`])\cr
#' The autotuner for which to enable validation.
#' @param validate (`numeric(1)`, `"predefined"`, `"test"`, or `NULL`)\cr
#' How to configure the validation during the hyperparameter tuning.
#' @param ... (any)\cr
#' Passed when calling `set_validate()` on the wrapped leaerner.
#' @export
#' @examples
#' at = auto_tuner(
#' tuner = tnr("random_search"),
#' learner = lrn("classif.debug", early_stopping = TRUE,
#' iter = to_tune(upper = 1000L, internal = TRUE), validate = 0.2),
#' resampling = rsmp("holdout")
#' )
#' # use the test set as validation data during tuning
#' set_validate(at, validate = "test")
#' at$learner$validate
set_validate.AutoTuner = function(learner, validate, ...) {
set_validate(learner$learner, validate = validate, ...)
invisible(learner)
}
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