R/Learner.R
In mlr-org/mlr3: Machine Learning in R - Next Generation
Defines functions
unmarshal_model.learner_state_marshaled
marshal_model.learner_state
default_values.Learner
get_log_condition
rd_info.Learner
#' @title Learner Class
#'
#' @include mlr_reflections.R
#'
#' @description
#' This is the abstract base class for learner objects like [LearnerClassif] and [LearnerRegr].
#'
#' Learners are build around the three following key parts:
#'
#' * Methods `$train()` and `$predict()` which call internal methods or private methods `$.train()`/`$.predict()`).
#' * A [paradox::ParamSet] which stores meta-information about available hyperparameters, and also stores hyperparameter settings.
#' * Meta-information about the requirements and capabilities of the learner.
#' * The fitted model stored in field `$model`, available after calling `$train()`.
#'
#' Predefined learners are stored in the [dictionary][mlr3misc::Dictionary] [mlr_learners],
#' e.g. [`classif.rpart`][mlr_learners_classif.rpart] or [`regr.rpart`][mlr_learners_regr.rpart].
#'
#' More classification and regression learners are implemented in the add-on package \CRANpkg{mlr3learners}.
#' Learners for survival analysis (or more general, for probabilistic regression) can be found in \CRANpkg{mlr3proba}.
#' Unsupervised cluster algorithms are implemented in \CRANpkg{mlr3cluster}.
#' The dictionary [mlr_learners] gets automatically populated with the new learners as soon as the respective packages are loaded.
#'
#' More (experimental) learners can be found in the GitHub repository: \url{https://github.com/mlr-org/mlr3extralearners}.
#' A guide on how to extend \CRANpkg{mlr3} with custom learners can be found in the [mlr3book](https://mlr3book.mlr-org.com).
#'
#' To combine the learner with preprocessing operations like factor encoding, \CRANpkg{mlr3pipelines} is recommended.
#' Hyperparameters stored in the `param_set` can be tuned with \CRANpkg{mlr3tuning}.
#'
#' @template param_id
#' @template param_task_type
#' @template param_param_set
#' @template param_predict_types
#' @template param_feature_types
#' @template param_learner_properties
#' @template param_data_formats
#' @template param_packages
#' @template param_label
#' @template param_man
#'
#'
#' @section Optional Extractors:
#'
#' Specific learner implementations are free to implement additional getters to ease the access of certain parts
#' of the model in the inherited subclasses.
#'
#' For the following operations, extractors are standardized:
#'
#' * `importance(...)`: Returns the feature importance score as numeric vector.
#' The higher the score, the more important the variable.
#' The returned vector is named with feature names and sorted in decreasing order.
#' Note that the model might omit features it has not used at all.
#' The learner must be tagged with property `"importance"`.
#' To filter variables using the importance scores, see package \CRANpkg{mlr3filters}.
#'
#' * `selected_features(...)`: Returns a subset of selected features as `character()`.
#' The learner must be tagged with property `"selected_features"`.
#'
#' * `oob_error(...)`: Returns the out-of-bag error of the model as `numeric(1)`.
#' The learner must be tagged with property `"oob_error"`.
#'
#' * `loglik(...)`: Extracts the log-likelihood (c.f. [stats::logLik()]).
#' This can be used in measures like [mlr_measures_aic] or [mlr_measures_bic].
#'
#' * `internal_valid_scores`: Returns the internal validation score(s) of the model as a named `list()`.
#' Only available for [`Learner`]s with the `"validation"` property.
#' If the learner is not trained yet, this returns `NULL`.
#'
#' * `internal_tuned_values`: Returns the internally tuned hyperparameters of the model as a named `list()`.
#' Only available for [`Learner`]s with the `"internal_tuning"` property.
#' If the learner is not trained yet, this returns `NULL`.
#'
#' @section Setting Hyperparameters:
#'
#' All information about hyperparameters is stored in the slot `param_set` which is a [paradox::ParamSet].
#' The printer gives an overview about the ids of available hyperparameters, their storage type, lower and upper bounds,
#' possible levels (for factors), default values and assigned values.
#' To set hyperparameters, assign a named list to the subslot `values`:
#' ```
#' lrn = lrn("classif.rpart")
#' lrn$param_set$values = list(minsplit = 3, cp = 0.01)
#' ```
#' Note that this operation replaces all previously set hyperparameter values.
#' If you only intend to change one specific hyperparameter value and leave the others as-is, you can use the helper function [mlr3misc::insert_named()]:
#' ```
#' lrn$param_set$values = mlr3misc::insert_named(lrn$param_set$values, list(cp = 0.001))
#' ```
#' If the learner has additional hyperparameters which are not encoded in the [ParamSet][paradox::ParamSet], you can easily extend the learner.
#' Here, we add a factor hyperparameter with id `"foo"` and possible levels `"a"` and `"b"`:
#' ```
#' lrn$param_set$add(paradox::ParamFct$new("foo", levels = c("a", "b")))
#' ```
#'
#' @section Implementing Validation:
#' Some Learners, such as `XGBoost`, other boosting algorithms, or deep learning models (`mlr3torch`),
#' utilize validation data during the training to prevent overfitting or to log the validation performance.
#' It is possible to configure learners to be able to receive such an independent validation set during training.
#' To do so, one must:
#' * annotate the learner with the `"validation"` property
#' * implement the active binding `$internal_valid_scores` (see section *Optional Extractors*), as well as the
#' private method `$.extract_internal_valid_scores()` which returns the (final) internal validation scores from the
#' model of the [`Learner`] and returns them as a named `list()` of `numeric(1)`.
#' If the model is not trained yet, this method should return `NULL`.
#' * Add the `validate` parameter, which can be either `NULL`, a ratio in $(0, 1)$, `"test"`, or `"predefined"`:
#' * `NULL`: no validation
#' * `ratio`: only proportion `1 - ratio` of the task is used for training and `ratio` is used for validation.
#' * `"test"` means that the `"test"` task is used.
#' **Warning**: This can lead to biased performance estimation.
#' This option is only available if the learner is being trained via [resample()], [benchmark()] or functions that
#' internally use them, e.g. [`mlr3tuning::tune()`] or [`mlr3batchmark::batchmark()`].
#' This is especially useful for hyperparameter tuning, where one might e.g. want to use the same validation data
#' for early stopping and model evaluation.
#' * `"predefined"` means that the task's (manually set) `$internal_valid_task` is used.
#' See the [`Task`] documentation for more information.
#'
#' For an example how to do this, see [`LearnerClassifDebug`].
#' Note that in `.train()`, the `$internal_valid_task` will only be present if the `$validate` field of the `Learner`
#' is set to a non-`NULL` value.
#'
#' @section Implementing Internal Tuning:
#' Some learners such as `XGBoost` or `cv.glmnet` can internally tune hyperparameters.
#' XGBoost, for example, can tune the number of boosting rounds based on the validation performance.
#' CV Glmnet, on the other hand, can tune the regularization parameter based on an internal cross-validation.
#' Internal tuning *can* therefore rely on the internal validation data, but does not necessarily do so.
#'
#' In order to be able to combine this internal hyperparamer tuning with the standard hyperparameter optimization
#' implemented via \CRANpkg{mlr3tuning}, one most:
#' * annotate the learner with the `"internal_tuning"` property
#' * implement the active binding `$internal_tuned_values` (see section *Optional Extractors*) as well as the
#' private method `$.extract_internal_tuned_values()` which extracts the internally tuned values from the [`Learner`]'s
#' model and returns them as a named `list()`.
#' If the model is not trained yet, this method should return `NULL`.
#' * Have at least one parameter tagged with `"internal_tuning"`, which requires to also provide a `in_tune_fn` and
#' `disable_tune_fn`, and *should* also include a default `aggr`egation function.
#'
#' For an example how to do this, see [`LearnerClassifDebug`].
#'
#' @section Implementing Marshaling:
#' Some [`Learner`]s have models that cannot be serialized as they e.g. contain external pointers.
#' In order to still be able to save them, use them with parallelization or callr encapsulation it is necessary
#' to implement how they should be (un)-marshaled. See [`marshaling`] for how to do this.
#'
#' @template seealso_learner
#' @export
Learner = R6Class("Learner",
public = list(
#' @template field_id
id = NULL,
#' @template field_label
label = NA_character_,
#' @field state (`NULL` | named `list()`)\cr
#' Current (internal) state of the learner.
#' Contains all information gathered during `train()` and `predict()`.
#' It is not recommended to access elements from `state` directly.
#' This is an internal data structure which may change in the future.
state = NULL,
#' @template field_task_type
task_type = NULL,
#' @field predict_types (`character()`)\cr
#' Stores the possible predict types the learner is capable of.
#' A complete list of candidate predict types, grouped by task type, is stored in [`mlr_reflections$learner_predict_types`][mlr_reflections].
predict_types = NULL,
#' @field feature_types (`character()`)\cr
#' Stores the feature types the learner can handle, e.g. `"logical"`, `"numeric"`, or `"factor"`.
#' A complete list of candidate feature types, grouped by task type, is stored in [`mlr_reflections$task_feature_types`][mlr_reflections].
feature_types = NULL,
#' @field properties (`character()`)\cr
#' Stores a set of properties/capabilities the learner has.
#' A complete list of candidate properties, grouped by task type, is stored in [`mlr_reflections$learner_properties`][mlr_reflections].
properties = NULL,
#' @field data_formats (`character()`)\cr
#' Supported data format, e.g. `"data.table"` or `"Matrix"`.
data_formats = NULL,
#' @template field_packages
packages = NULL,
#' @template field_predict_sets
predict_sets = "test",
#' @field parallel_predict (`logical(1)`)\cr
#' If set to `TRUE`, use \CRANpkg{future} to calculate predictions in parallel (default: `FALSE`).
#' The row ids of the `task` will be split into [future::nbrOfWorkers()] chunks,
#' and predictions are evaluated according to the active [future::plan()].
#' This currently only works for methods `Learner$predict()` and `Learner$predict_newdata()`,
#' and has no effect during [resample()] or [benchmark()] where you have other means
#' to parallelize.
parallel_predict = FALSE,
#' @field timeout (named `numeric(2)`)\cr
#' Timeout for the learner's train and predict steps, in seconds.
#' This works differently for different encapsulation methods, see
#' [mlr3misc::encapsulate()].
#' Default is `c(train = Inf, predict = Inf)`.
#' Also see the section on error handling the mlr3book:
#' \url{https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-error-handling}
timeout = c(train = Inf, predict = Inf),
#' @template field_man
man = NULL,
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' Note that this object is typically constructed via a derived classes, e.g. [LearnerClassif] or [LearnerRegr].
initialize = function(id, task_type, param_set = ps(), predict_types = character(), feature_types = character(),
properties = character(), data_formats = "data.table", packages = character(), label = NA_character_, man = NA_character_) {
self$id = assert_string(id, min.chars = 1L)
self$label = assert_string(label, na.ok = TRUE)
self$task_type = assert_choice(task_type, mlr_reflections$task_types$type)
private$.param_set = assert_param_set(param_set)
self$feature_types = assert_ordered_set(feature_types, mlr_reflections$task_feature_types, .var.name = "feature_types")
self$predict_types = assert_ordered_set(predict_types, names(mlr_reflections$learner_predict_types[[task_type]]),
empty.ok = FALSE, .var.name = "predict_types")
private$.predict_type = predict_types[1L]
self$properties = sort(assert_subset(properties, mlr_reflections$learner_properties[[task_type]]))
self$data_formats = assert_subset(data_formats, mlr_reflections$data_formats)
self$packages = union("mlr3", assert_character(packages, any.missing = FALSE, min.chars = 1L))
self$man = assert_string(man, na.ok = TRUE)
check_packages_installed(packages, msg = sprintf("Package '%%s' required but not installed for Learner '%s'", id))
},
#' @description
#' Helper for print outputs.
#' @param ... (ignored).
format = function(...) {
sprintf("<%s:%s>", class(self)[1L], self$id)
},
#' @description
#' Printer.
#' @param ... (ignored).
print = function(...) {
catn(format(self), if (is.null(self$label) || is.na(self$label)) "" else paste0(": ", self$label))
catn(str_indent("* Model:", if (is.null(self$model)) "-" else if (is_marshaled_model(self$model)) "<marshaled>" else paste0(class(self$model)[1L])))
catn(str_indent("* Parameters:", as_short_string(self$param_set$values, 1000L)))
if (exists("validate", self)) catn(str_indent("* Validate:", format(self$validate)))
catn(str_indent("* Packages:", self$packages))
catn(str_indent("* Predict Types: ", replace(self$predict_types, self$predict_types == self$predict_type, paste0("[", self$predict_type, "]"))))
catn(str_indent("* Feature Types:", self$feature_types))
catn(str_indent("* Properties:", self$properties))
w = self$warnings
e = self$errors
if (length(w)) {
catn(str_indent("* Warnings:", w))
}
if (length(e)) {
catn(str_indent("* Errors:", e))
}
},
#' @description
#' Opens the corresponding help page referenced by field `$man`.
help = function() {
open_help(self$man)
},
#' @description
#' Train the learner on a set of observations of the provided `task`.
#' Mutates the learner by reference, i.e. stores the model alongside other information in field `$state`.
#'
#' @param task ([Task]).
#'
#' @param row_ids (`integer()`)\cr
#' Vector of training indices as subset of `task$row_ids`.
#' For a simple split into training and test set, see [partition()].
#'
#' @return
#' Returns the object itself, but modified **by reference**.
#' You need to explicitly `$clone()` the object beforehand if you want to keeps
#' the object in its previous state.
train = function(task, row_ids = NULL) {
task = assert_task(as_task(task))
assert_learnable(task, self)
row_ids = assert_row_ids(row_ids, null.ok = TRUE)
if (!is.null(self$hotstart_stack)) {
# search for hotstart learner
start_learner = get_private(self$hotstart_stack)$.start_learner(self, task$hash)
}
if (is.null(self$hotstart_stack) || is.null(start_learner)) {
# no hotstart learners stored or no adaptable model found
learner = self
mode = "train"
} else {
self$state = start_learner$clone()$state
learner = self
mode = "hotstart"
}
train_row_ids = if (!is.null(row_ids)) row_ids else task$row_roles$use
train_result = learner_train(learner, task, train_row_ids = train_row_ids, mode = mode)
self$model = unmarshal_model(model = self$state$model, inplace = TRUE)
# store data prototype
proto = task$data(rows = integer())
self$state$data_prototype = proto
self$state$task_prototype = proto
# store the task w/o the data
self$state$train_task = task_rm_backend(task$clone(deep = TRUE))
invisible(self)
},
#' @description
#' Uses the information stored during `$train()` in `$state` to create a new [Prediction]
#' for a set of observations of the provided `task`.
#'
#' @param task ([Task]).
#'
#' @param row_ids (`integer()`)\cr
#' Vector of test indices as subset of `task$row_ids`.
#' For a simple split into training and test set, see [partition()].
#'
#' @return [Prediction].
predict = function(task, row_ids = NULL) {
# improve error message for the common mistake of passing a data.frame here
if (is.data.frame(task)) {
stopf("To predict on data.frames, use the method `$predict_newdata()` instead of `$predict()`")
}
task = assert_task(as_task(task))
assert_predictable(task, self)
row_ids = assert_row_ids(row_ids, null.ok = TRUE)
if (is.null(self$state$model) && is.null(self$state$fallback_state$model)) {
stopf("Cannot predict, Learner '%s' has not been trained yet", self$id)
}
# we need to marshal for call-r prediction and parallel prediction, but afterwards we reset the model
# to it original state
model_was_marshaled = is_marshaled_model(self$model)
on.exit({
if (model_was_marshaled) {
self$model = marshal_model(self$model, inplace = TRUE)
} else {
self$model = unmarshal_model(self$model, inplace = TRUE)
}
}, add = TRUE)
# we only have to marshal here for the parallel prediction case, because learner_predict() handles the
# marshaling for call-r encapsulation itself
if (isTRUE(self$parallel_predict) && nbrOfWorkers() > 1L) {
row_ids = row_ids %??% task$row_ids
chunked = chunk_vector(row_ids, n_chunks = nbrOfWorkers(), shuffle = FALSE)
self$model = marshal_model(self$model, inplace = TRUE)
pdata = future.apply::future_lapply(chunked,
learner_predict, learner = self, task = task,
future.globals = FALSE, future.seed = TRUE)
pdata = do.call(c, pdata)
} else {
pdata = learner_predict(self, task, row_ids)
}
if (is.null(pdata)) {
return(NULL)
} else {
as_prediction(pdata)
}
},
#' @description
#' Uses the model fitted during `$train()` to create a new [Prediction] based on the new data in `newdata`.
#' Object `task` is the task used during `$train()` and required for conversion of `newdata`.
#' If the learner's `$train()` method has been called, there is a (size reduced) version
#' of the training task stored in the learner.
#' If the learner has been fitted via [resample()] or [benchmark()], you need to pass the corresponding task stored
#' in the [ResampleResult] or [BenchmarkResult], respectively.
#'
#' @param newdata (any object supported by [as_data_backend()])\cr
#' New data to predict on.
#' All data formats convertible by [as_data_backend()] are supported, e.g.
#' `data.frame()` or [DataBackend].
#' If a [DataBackend] is provided as `newdata`, the row ids are preserved,
#' otherwise they are set to to the sequence `1:nrow(newdata)`.
#'
#' @param task ([Task]).
#'
#' @return [Prediction].
predict_newdata = function(newdata, task = NULL) {
if (is.null(task)) {
if (is.null(self$state$train_task)) {
stopf("No task stored, and no task provided")
}
task = self$state$train_task$clone()
} else {
task = assert_task(as_task(task, clone = TRUE))
assert_learnable(task, self)
task = task_rm_backend(task)
}
newdata = as_data_backend(newdata)
assert_names(newdata$colnames, must.include = task$feature_names)
# the following columns are automatically set to NA if missing
impute = unlist(task$col_roles[c("target", "name", "order", "stratum", "group", "weight")], use.names = FALSE)
impute = setdiff(impute, newdata$colnames)
if (length(impute)) {
# create list with correct NA types and cbind it to the backend
ci = insert_named(task$col_info[list(impute), c("id", "type", "levels"), on = "id", with = FALSE], list(value = NA))
na_cols = set_names(pmap(ci, function(..., nrow) rep(auto_convert(...), nrow), nrow = newdata$nrow), ci$id)
tab = invoke(data.table, .args = insert_named(na_cols, set_names(list(newdata$rownames), newdata$primary_key)))
newdata = DataBackendCbind$new(newdata, DataBackendDataTable$new(tab, primary_key = newdata$primary_key))
}
# do some type conversions if necessary
task$backend = newdata
task$row_roles$use = task$backend$rownames
self$predict(task)
},
#' @description
#' Reset the learner, i.e. un-train by resetting the `state`.
#'
#' @return
#' Returns the object itself, but modified **by reference**.
#' You need to explicitly `$clone()` the object beforehand if you want to keeps
#' the object in its previous state.
reset = function() {
self$state = NULL
invisible(self)
},
#' @description
#' Extracts the base learner from nested learner objects like
#' `GraphLearner` in \CRANpkg{mlr3pipelines} or `AutoTuner` in
#' \CRANpkg{mlr3tuning}.
#' Returns the [Learner] itself for regular learners.
#'
#' @param recursive (`integer(1)`)\cr
#' Depth of recursion for multiple nested objects.
#'
#' @return [Learner].
base_learner = function(recursive = Inf) {
if (exists(".base_learner", envir = private, inherits = FALSE)) {
private$.base_learner(recursive)
} else {
self
}
}
),
active = list(
#' @field model (any)\cr
#' The fitted model. Only available after `$train()` has been called.
model = function(rhs) {
if (!missing(rhs)) {
self$state$model = rhs
}
self$state$model
},
#' @field timings (named `numeric(2)`)\cr
#' Elapsed time in seconds for the steps `"train"` and `"predict"`.
#' Measured via [mlr3misc::encapsulate()].
timings = function(rhs) {
assert_ro_binding(rhs)
set_names(c(self$state$train_time %??% NA_real_, self$state$predict_time %??% NA_real_), c("train", "predict"))
},
#' @field log ([data.table::data.table()])\cr
#' Returns the output (including warning and errors) as table with columns
#'
#' * `"stage"` ("train" or "predict"),
#' * `"class"` ("output", "warning", or "error"), and
#' * `"msg"` (`character()`).
log = function(rhs) {
assert_ro_binding(rhs)
self$state$log
},
#' @field warnings (`character()`)\cr
#' Logged warnings as vector.
warnings = function(rhs) {
assert_ro_binding(rhs)
get_log_condition(self$state, "warning")
},
#' @field errors (`character()`)\cr
#' Logged errors as vector.
errors = function(rhs) {
assert_ro_binding(rhs)
get_log_condition(self$state, "error")
},
#' @template field_hash
hash = function(rhs) {
assert_ro_binding(rhs)
calculate_hash(class(self), self$id, self$param_set$values, private$.predict_type,
self$fallback$hash, self$parallel_predict, get0("validate", self))
},
#' @field phash (`character(1)`)\cr
#' Hash (unique identifier) for this partial object, excluding some components
#' which are varied systematically during tuning (parameter values).
phash = function(rhs) {
assert_ro_binding(rhs)
calculate_hash(class(self), self$id, private$.predict_type,
self$fallback$hash, self$parallel_predict, get0("validate", self))
},
#' @field predict_type (`character(1)`)\cr
#' Stores the currently active predict type, e.g. `"response"`.
#' Must be an element of `$predict_types`.
predict_type = function(rhs) {
if (missing(rhs)) {
return(private$.predict_type)
}
assert_string(rhs, .var.name = "predict_type")
if (rhs %nin% self$predict_types) {
stopf("Learner '%s' does not support predict type '%s'", self$id, rhs)
}
private$.predict_type = rhs
},
#' @template field_param_set
param_set = function(rhs) {
if (!missing(rhs) && !identical(rhs, private$.param_set)) {
stop("param_set is read-only.")
}
private$.param_set
},
#' @field encapsulate (named `character()`)\cr
#' Controls how to execute the code in internal train and predict methods.
#' Must be a named character vector with names `"train"` and `"predict"`.
#' Possible values are `"none"`, `"try"`, `"evaluate"` (requires package \CRANpkg{evaluate}) and `"callr"` (requires package \CRANpkg{callr}).
#' See [mlr3misc::encapsulate()] for more details.
encapsulate = function(rhs) {
default = c(train = "none", predict = "none")
if (missing(rhs)) {
return(insert_named(default, private$.encapsulate))
}
assert_character(rhs)
assert_names(names(rhs), subset.of = c("train", "predict"))
private$.encapsulate = insert_named(default, rhs)
},
#' @field fallback ([Learner])\cr
#' Learner which is fitted to impute predictions in case that either the model fitting or the prediction of the top learner is not successful.
#' Requires encapsulation, otherwise errors are not caught and the execution is terminated before the fallback learner kicks in.
#' If you have not set encapsulation manually before, setting the fallback learner automatically
#' activates encapsulation using the \CRANpkg{evaluate} package.
#' Also see the section on error handling the mlr3book:
#' \url{https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-error-handling}
fallback = function(rhs) {
if (missing(rhs)) {
return(private$.fallback)
}
if (!is.null(rhs)) {
assert_learner(rhs, task_type = self$task_type)
if (!identical(self$predict_type, rhs$predict_type)) {
warningf("The fallback learner '%s' and the base learner '%s' have different predict types: '%s' != '%s'.",
rhs$id, self$id, rhs$predict_type, self$predict_type)
}
if (is.null(private$.encapsulate)) {
private$.encapsulate = c(train = "evaluate", predict = "evaluate")
}
}
private$.fallback = rhs
},
#' @field hotstart_stack ([HotstartStack])\cr.
#' Stores `HotstartStack`.
hotstart_stack = function(rhs) {
if (missing(rhs)) {
return(private$.hotstart_stack)
}
assert_r6(rhs, "HotstartStack", null.ok = TRUE)
private$.hotstart_stack = rhs
}
),
private = list(
.encapsulate = NULL,
.fallback = NULL,
.predict_type = NULL,
.param_set = NULL,
.hotstart_stack = NULL,
deep_clone = function(name, value) {
switch(name,
.param_set = value$clone(deep = TRUE),
.fallback = if (is.null(value)) NULL else value$clone(deep = TRUE),
state = {
if (!is.null(value$train_task)) {
value$train_task = value$train_task$clone(deep = TRUE)
}
value$log = copy(value$log)
value
},
value
)
}
)
)
#' @export
rd_info.Learner = function(obj, ...) {
x = c("",
sprintf("* Task type: %s", rd_format_string(obj$task_type)),
sprintf("* Predict Types: %s", rd_format_string(obj$predict_types)),
sprintf("* Feature Types: %s", rd_format_string(obj$feature_types)),
sprintf("* Required Packages: %s", rd_format_packages(obj$packages))
)
paste(x, collapse = "\n")
}
get_log_condition = function(state, condition) {
if (is.null(state$log)) {
character()
} else {
fget(state$log, i = condition, j = "msg", key = "class")
}
}
#' @export
default_values.Learner = function(x, search_space, task, ...) { # nolint
values = default_values(x$param_set)
if (any(search_space$ids() %nin% names(values))) {
stopf("Could not find default values for the following parameters: %s",
str_collapse(setdiff(search_space$ids(), names(values))))
}
values[search_space$ids()]
}
# #' @export
# format_list_item.Learner = function(x, ...) { # nolint
# sprintf("<lrn:%s>", x$id)
# }
#' @export
marshal_model.learner_state = function(model, inplace = FALSE, ...) {
if (is.null(model$model)) {
return(model)
}
mm = marshal_model(model$model, inplace = inplace, ...)
if (!is_marshaled_model(mm)) {
return(model)
}
model$model = mm
structure(list(
marshaled = model,
packages = "mlr3"
), class = c("learner_state_marshaled", "list_marshaled", "marshaled"))
}
#' @export
unmarshal_model.learner_state_marshaled = function(model, inplace = FALSE, ...) {
mm = model$marshaled
mm$model = unmarshal_model(mm$model, inplace = inplace, ...)
return(mm)
}
mlr-org/mlr3 documentation built on July 10, 2024, 10:53 a.m.
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Defines functions unmarshal_model.learner_state_marshaled marshal_model.learner_state default_values.Learner get_log_condition rd_info.Learner
#' @title Learner Class
#'
#' @include mlr_reflections.R
#'
#' @description
#' This is the abstract base class for learner objects like [LearnerClassif] and [LearnerRegr].
#'
#' Learners are build around the three following key parts:
#'
#' * Methods `$train()` and `$predict()` which call internal methods or private methods `$.train()`/`$.predict()`).
#' * A [paradox::ParamSet] which stores meta-information about available hyperparameters, and also stores hyperparameter settings.
#' * Meta-information about the requirements and capabilities of the learner.
#' * The fitted model stored in field `$model`, available after calling `$train()`.
#'
#' Predefined learners are stored in the [dictionary][mlr3misc::Dictionary] [mlr_learners],
#' e.g. [`classif.rpart`][mlr_learners_classif.rpart] or [`regr.rpart`][mlr_learners_regr.rpart].
#'
#' More classification and regression learners are implemented in the add-on package \CRANpkg{mlr3learners}.
#' Learners for survival analysis (or more general, for probabilistic regression) can be found in \CRANpkg{mlr3proba}.
#' Unsupervised cluster algorithms are implemented in \CRANpkg{mlr3cluster}.
#' The dictionary [mlr_learners] gets automatically populated with the new learners as soon as the respective packages are loaded.
#'
#' More (experimental) learners can be found in the GitHub repository: \url{https://github.com/mlr-org/mlr3extralearners}.
#' A guide on how to extend \CRANpkg{mlr3} with custom learners can be found in the [mlr3book](https://mlr3book.mlr-org.com).
#'
#' To combine the learner with preprocessing operations like factor encoding, \CRANpkg{mlr3pipelines} is recommended.
#' Hyperparameters stored in the `param_set` can be tuned with \CRANpkg{mlr3tuning}.
#'
#' @template param_id
#' @template param_task_type
#' @template param_param_set
#' @template param_predict_types
#' @template param_feature_types
#' @template param_learner_properties
#' @template param_data_formats
#' @template param_packages
#' @template param_label
#' @template param_man
#'
#'
#' @section Optional Extractors:
#'
#' Specific learner implementations are free to implement additional getters to ease the access of certain parts
#' of the model in the inherited subclasses.
#'
#' For the following operations, extractors are standardized:
#'
#' * `importance(...)`: Returns the feature importance score as numeric vector.
#' The higher the score, the more important the variable.
#' The returned vector is named with feature names and sorted in decreasing order.
#' Note that the model might omit features it has not used at all.
#' The learner must be tagged with property `"importance"`.
#' To filter variables using the importance scores, see package \CRANpkg{mlr3filters}.
#'
#' * `selected_features(...)`: Returns a subset of selected features as `character()`.
#' The learner must be tagged with property `"selected_features"`.
#'
#' * `oob_error(...)`: Returns the out-of-bag error of the model as `numeric(1)`.
#' The learner must be tagged with property `"oob_error"`.
#'
#' * `loglik(...)`: Extracts the log-likelihood (c.f. [stats::logLik()]).
#' This can be used in measures like [mlr_measures_aic] or [mlr_measures_bic].
#'
#' * `internal_valid_scores`: Returns the internal validation score(s) of the model as a named `list()`.
#' Only available for [`Learner`]s with the `"validation"` property.
#' If the learner is not trained yet, this returns `NULL`.
#'
#' * `internal_tuned_values`: Returns the internally tuned hyperparameters of the model as a named `list()`.
#' Only available for [`Learner`]s with the `"internal_tuning"` property.
#' If the learner is not trained yet, this returns `NULL`.
#'
#' @section Setting Hyperparameters:
#'
#' All information about hyperparameters is stored in the slot `param_set` which is a [paradox::ParamSet].
#' The printer gives an overview about the ids of available hyperparameters, their storage type, lower and upper bounds,
#' possible levels (for factors), default values and assigned values.
#' To set hyperparameters, assign a named list to the subslot `values`:
#' ```
#' lrn = lrn("classif.rpart")
#' lrn$param_set$values = list(minsplit = 3, cp = 0.01)
#' ```
#' Note that this operation replaces all previously set hyperparameter values.
#' If you only intend to change one specific hyperparameter value and leave the others as-is, you can use the helper function [mlr3misc::insert_named()]:
#' ```
#' lrn$param_set$values = mlr3misc::insert_named(lrn$param_set$values, list(cp = 0.001))
#' ```
#' If the learner has additional hyperparameters which are not encoded in the [ParamSet][paradox::ParamSet], you can easily extend the learner.
#' Here, we add a factor hyperparameter with id `"foo"` and possible levels `"a"` and `"b"`:
#' ```
#' lrn$param_set$add(paradox::ParamFct$new("foo", levels = c("a", "b")))
#' ```
#'
#' @section Implementing Validation:
#' Some Learners, such as `XGBoost`, other boosting algorithms, or deep learning models (`mlr3torch`),
#' utilize validation data during the training to prevent overfitting or to log the validation performance.
#' It is possible to configure learners to be able to receive such an independent validation set during training.
#' To do so, one must:
#' * annotate the learner with the `"validation"` property
#' * implement the active binding `$internal_valid_scores` (see section *Optional Extractors*), as well as the
#' private method `$.extract_internal_valid_scores()` which returns the (final) internal validation scores from the
#' model of the [`Learner`] and returns them as a named `list()` of `numeric(1)`.
#' If the model is not trained yet, this method should return `NULL`.
#' * Add the `validate` parameter, which can be either `NULL`, a ratio in $(0, 1)$, `"test"`, or `"predefined"`:
#' * `NULL`: no validation
#' * `ratio`: only proportion `1 - ratio` of the task is used for training and `ratio` is used for validation.
#' * `"test"` means that the `"test"` task is used.
#' **Warning**: This can lead to biased performance estimation.
#' This option is only available if the learner is being trained via [resample()], [benchmark()] or functions that
#' internally use them, e.g. [`mlr3tuning::tune()`] or [`mlr3batchmark::batchmark()`].
#' This is especially useful for hyperparameter tuning, where one might e.g. want to use the same validation data
#' for early stopping and model evaluation.
#' * `"predefined"` means that the task's (manually set) `$internal_valid_task` is used.
#' See the [`Task`] documentation for more information.
#'
#' For an example how to do this, see [`LearnerClassifDebug`].
#' Note that in `.train()`, the `$internal_valid_task` will only be present if the `$validate` field of the `Learner`
#' is set to a non-`NULL` value.
#'
#' @section Implementing Internal Tuning:
#' Some learners such as `XGBoost` or `cv.glmnet` can internally tune hyperparameters.
#' XGBoost, for example, can tune the number of boosting rounds based on the validation performance.
#' CV Glmnet, on the other hand, can tune the regularization parameter based on an internal cross-validation.
#' Internal tuning *can* therefore rely on the internal validation data, but does not necessarily do so.
#'
#' In order to be able to combine this internal hyperparamer tuning with the standard hyperparameter optimization
#' implemented via \CRANpkg{mlr3tuning}, one most:
#' * annotate the learner with the `"internal_tuning"` property
#' * implement the active binding `$internal_tuned_values` (see section *Optional Extractors*) as well as the
#' private method `$.extract_internal_tuned_values()` which extracts the internally tuned values from the [`Learner`]'s
#' model and returns them as a named `list()`.
#' If the model is not trained yet, this method should return `NULL`.
#' * Have at least one parameter tagged with `"internal_tuning"`, which requires to also provide a `in_tune_fn` and
#' `disable_tune_fn`, and *should* also include a default `aggr`egation function.
#'
#' For an example how to do this, see [`LearnerClassifDebug`].
#'
#' @section Implementing Marshaling:
#' Some [`Learner`]s have models that cannot be serialized as they e.g. contain external pointers.
#' In order to still be able to save them, use them with parallelization or callr encapsulation it is necessary
#' to implement how they should be (un)-marshaled. See [`marshaling`] for how to do this.
#'
#' @template seealso_learner
#' @export
Learner = R6Class("Learner",
public = list(
#' @template field_id
id = NULL,
#' @template field_label
label = NA_character_,
#' @field state (`NULL` | named `list()`)\cr
#' Current (internal) state of the learner.
#' Contains all information gathered during `train()` and `predict()`.
#' It is not recommended to access elements from `state` directly.
#' This is an internal data structure which may change in the future.
state = NULL,
#' @template field_task_type
task_type = NULL,
#' @field predict_types (`character()`)\cr
#' Stores the possible predict types the learner is capable of.
#' A complete list of candidate predict types, grouped by task type, is stored in [`mlr_reflections$learner_predict_types`][mlr_reflections].
predict_types = NULL,
#' @field feature_types (`character()`)\cr
#' Stores the feature types the learner can handle, e.g. `"logical"`, `"numeric"`, or `"factor"`.
#' A complete list of candidate feature types, grouped by task type, is stored in [`mlr_reflections$task_feature_types`][mlr_reflections].
feature_types = NULL,
#' @field properties (`character()`)\cr
#' Stores a set of properties/capabilities the learner has.
#' A complete list of candidate properties, grouped by task type, is stored in [`mlr_reflections$learner_properties`][mlr_reflections].
properties = NULL,
#' @field data_formats (`character()`)\cr
#' Supported data format, e.g. `"data.table"` or `"Matrix"`.
data_formats = NULL,
#' @template field_packages
packages = NULL,
#' @template field_predict_sets
predict_sets = "test",
#' @field parallel_predict (`logical(1)`)\cr
#' If set to `TRUE`, use \CRANpkg{future} to calculate predictions in parallel (default: `FALSE`).
#' The row ids of the `task` will be split into [future::nbrOfWorkers()] chunks,
#' and predictions are evaluated according to the active [future::plan()].
#' This currently only works for methods `Learner$predict()` and `Learner$predict_newdata()`,
#' and has no effect during [resample()] or [benchmark()] where you have other means
#' to parallelize.
parallel_predict = FALSE,
#' @field timeout (named `numeric(2)`)\cr
#' Timeout for the learner's train and predict steps, in seconds.
#' This works differently for different encapsulation methods, see
#' [mlr3misc::encapsulate()].
#' Default is `c(train = Inf, predict = Inf)`.
#' Also see the section on error handling the mlr3book:
#' \url{https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-error-handling}
timeout = c(train = Inf, predict = Inf),
#' @template field_man
man = NULL,
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' Note that this object is typically constructed via a derived classes, e.g. [LearnerClassif] or [LearnerRegr].
initialize = function(id, task_type, param_set = ps(), predict_types = character(), feature_types = character(),
properties = character(), data_formats = "data.table", packages = character(), label = NA_character_, man = NA_character_) {
self$id = assert_string(id, min.chars = 1L)
self$label = assert_string(label, na.ok = TRUE)
self$task_type = assert_choice(task_type, mlr_reflections$task_types$type)
private$.param_set = assert_param_set(param_set)
self$feature_types = assert_ordered_set(feature_types, mlr_reflections$task_feature_types, .var.name = "feature_types")
self$predict_types = assert_ordered_set(predict_types, names(mlr_reflections$learner_predict_types[[task_type]]),
empty.ok = FALSE, .var.name = "predict_types")
private$.predict_type = predict_types[1L]
self$properties = sort(assert_subset(properties, mlr_reflections$learner_properties[[task_type]]))
self$data_formats = assert_subset(data_formats, mlr_reflections$data_formats)
self$packages = union("mlr3", assert_character(packages, any.missing = FALSE, min.chars = 1L))
self$man = assert_string(man, na.ok = TRUE)
check_packages_installed(packages, msg = sprintf("Package '%%s' required but not installed for Learner '%s'", id))
},
#' @description
#' Helper for print outputs.
#' @param ... (ignored).
format = function(...) {
sprintf("<%s:%s>", class(self)[1L], self$id)
},
#' @description
#' Printer.
#' @param ... (ignored).
print = function(...) {
catn(format(self), if (is.null(self$label) || is.na(self$label)) "" else paste0(": ", self$label))
catn(str_indent("* Model:", if (is.null(self$model)) "-" else if (is_marshaled_model(self$model)) "<marshaled>" else paste0(class(self$model)[1L])))
catn(str_indent("* Parameters:", as_short_string(self$param_set$values, 1000L)))
if (exists("validate", self)) catn(str_indent("* Validate:", format(self$validate)))
catn(str_indent("* Packages:", self$packages))
catn(str_indent("* Predict Types: ", replace(self$predict_types, self$predict_types == self$predict_type, paste0("[", self$predict_type, "]"))))
catn(str_indent("* Feature Types:", self$feature_types))
catn(str_indent("* Properties:", self$properties))
w = self$warnings
e = self$errors
if (length(w)) {
catn(str_indent("* Warnings:", w))
}
if (length(e)) {
catn(str_indent("* Errors:", e))
}
},
#' @description
#' Opens the corresponding help page referenced by field `$man`.
help = function() {
open_help(self$man)
},
#' @description
#' Train the learner on a set of observations of the provided `task`.
#' Mutates the learner by reference, i.e. stores the model alongside other information in field `$state`.
#'
#' @param task ([Task]).
#'
#' @param row_ids (`integer()`)\cr
#' Vector of training indices as subset of `task$row_ids`.
#' For a simple split into training and test set, see [partition()].
#'
#' @return
#' Returns the object itself, but modified **by reference**.
#' You need to explicitly `$clone()` the object beforehand if you want to keeps
#' the object in its previous state.
train = function(task, row_ids = NULL) {
task = assert_task(as_task(task))
assert_learnable(task, self)
row_ids = assert_row_ids(row_ids, null.ok = TRUE)
if (!is.null(self$hotstart_stack)) {
# search for hotstart learner
start_learner = get_private(self$hotstart_stack)$.start_learner(self, task$hash)
}
if (is.null(self$hotstart_stack) || is.null(start_learner)) {
# no hotstart learners stored or no adaptable model found
learner = self
mode = "train"
} else {
self$state = start_learner$clone()$state
learner = self
mode = "hotstart"
}
train_row_ids = if (!is.null(row_ids)) row_ids else task$row_roles$use
train_result = learner_train(learner, task, train_row_ids = train_row_ids, mode = mode)
self$model = unmarshal_model(model = self$state$model, inplace = TRUE)
# store data prototype
proto = task$data(rows = integer())
self$state$data_prototype = proto
self$state$task_prototype = proto
# store the task w/o the data
self$state$train_task = task_rm_backend(task$clone(deep = TRUE))
invisible(self)
},
#' @description
#' Uses the information stored during `$train()` in `$state` to create a new [Prediction]
#' for a set of observations of the provided `task`.
#'
#' @param task ([Task]).
#'
#' @param row_ids (`integer()`)\cr
#' Vector of test indices as subset of `task$row_ids`.
#' For a simple split into training and test set, see [partition()].
#'
#' @return [Prediction].
predict = function(task, row_ids = NULL) {
# improve error message for the common mistake of passing a data.frame here
if (is.data.frame(task)) {
stopf("To predict on data.frames, use the method `$predict_newdata()` instead of `$predict()`")
}
task = assert_task(as_task(task))
assert_predictable(task, self)
row_ids = assert_row_ids(row_ids, null.ok = TRUE)
if (is.null(self$state$model) && is.null(self$state$fallback_state$model)) {
stopf("Cannot predict, Learner '%s' has not been trained yet", self$id)
}
# we need to marshal for call-r prediction and parallel prediction, but afterwards we reset the model
# to it original state
model_was_marshaled = is_marshaled_model(self$model)
on.exit({
if (model_was_marshaled) {
self$model = marshal_model(self$model, inplace = TRUE)
} else {
self$model = unmarshal_model(self$model, inplace = TRUE)
}
}, add = TRUE)
# we only have to marshal here for the parallel prediction case, because learner_predict() handles the
# marshaling for call-r encapsulation itself
if (isTRUE(self$parallel_predict) && nbrOfWorkers() > 1L) {
row_ids = row_ids %??% task$row_ids
chunked = chunk_vector(row_ids, n_chunks = nbrOfWorkers(), shuffle = FALSE)
self$model = marshal_model(self$model, inplace = TRUE)
pdata = future.apply::future_lapply(chunked,
learner_predict, learner = self, task = task,
future.globals = FALSE, future.seed = TRUE)
pdata = do.call(c, pdata)
} else {
pdata = learner_predict(self, task, row_ids)
}
if (is.null(pdata)) {
return(NULL)
} else {
as_prediction(pdata)
}
},
#' @description
#' Uses the model fitted during `$train()` to create a new [Prediction] based on the new data in `newdata`.
#' Object `task` is the task used during `$train()` and required for conversion of `newdata`.
#' If the learner's `$train()` method has been called, there is a (size reduced) version
#' of the training task stored in the learner.
#' If the learner has been fitted via [resample()] or [benchmark()], you need to pass the corresponding task stored
#' in the [ResampleResult] or [BenchmarkResult], respectively.
#'
#' @param newdata (any object supported by [as_data_backend()])\cr
#' New data to predict on.
#' All data formats convertible by [as_data_backend()] are supported, e.g.
#' `data.frame()` or [DataBackend].
#' If a [DataBackend] is provided as `newdata`, the row ids are preserved,
#' otherwise they are set to to the sequence `1:nrow(newdata)`.
#'
#' @param task ([Task]).
#'
#' @return [Prediction].
predict_newdata = function(newdata, task = NULL) {
if (is.null(task)) {
if (is.null(self$state$train_task)) {
stopf("No task stored, and no task provided")
}
task = self$state$train_task$clone()
} else {
task = assert_task(as_task(task, clone = TRUE))
assert_learnable(task, self)
task = task_rm_backend(task)
}
newdata = as_data_backend(newdata)
assert_names(newdata$colnames, must.include = task$feature_names)
# the following columns are automatically set to NA if missing
impute = unlist(task$col_roles[c("target", "name", "order", "stratum", "group", "weight")], use.names = FALSE)
impute = setdiff(impute, newdata$colnames)
if (length(impute)) {
# create list with correct NA types and cbind it to the backend
ci = insert_named(task$col_info[list(impute), c("id", "type", "levels"), on = "id", with = FALSE], list(value = NA))
na_cols = set_names(pmap(ci, function(..., nrow) rep(auto_convert(...), nrow), nrow = newdata$nrow), ci$id)
tab = invoke(data.table, .args = insert_named(na_cols, set_names(list(newdata$rownames), newdata$primary_key)))
newdata = DataBackendCbind$new(newdata, DataBackendDataTable$new(tab, primary_key = newdata$primary_key))
}
# do some type conversions if necessary
task$backend = newdata
task$row_roles$use = task$backend$rownames
self$predict(task)
},
#' @description
#' Reset the learner, i.e. un-train by resetting the `state`.
#'
#' @return
#' Returns the object itself, but modified **by reference**.
#' You need to explicitly `$clone()` the object beforehand if you want to keeps
#' the object in its previous state.
reset = function() {
self$state = NULL
invisible(self)
},
#' @description
#' Extracts the base learner from nested learner objects like
#' `GraphLearner` in \CRANpkg{mlr3pipelines} or `AutoTuner` in
#' \CRANpkg{mlr3tuning}.
#' Returns the [Learner] itself for regular learners.
#'
#' @param recursive (`integer(1)`)\cr
#' Depth of recursion for multiple nested objects.
#'
#' @return [Learner].
base_learner = function(recursive = Inf) {
if (exists(".base_learner", envir = private, inherits = FALSE)) {
private$.base_learner(recursive)
} else {
self
}
}
),
active = list(
#' @field model (any)\cr
#' The fitted model. Only available after `$train()` has been called.
model = function(rhs) {
if (!missing(rhs)) {
self$state$model = rhs
}
self$state$model
},
#' @field timings (named `numeric(2)`)\cr
#' Elapsed time in seconds for the steps `"train"` and `"predict"`.
#' Measured via [mlr3misc::encapsulate()].
timings = function(rhs) {
assert_ro_binding(rhs)
set_names(c(self$state$train_time %??% NA_real_, self$state$predict_time %??% NA_real_), c("train", "predict"))
},
#' @field log ([data.table::data.table()])\cr
#' Returns the output (including warning and errors) as table with columns
#'
#' * `"stage"` ("train" or "predict"),
#' * `"class"` ("output", "warning", or "error"), and
#' * `"msg"` (`character()`).
log = function(rhs) {
assert_ro_binding(rhs)
self$state$log
},
#' @field warnings (`character()`)\cr
#' Logged warnings as vector.
warnings = function(rhs) {
assert_ro_binding(rhs)
get_log_condition(self$state, "warning")
},
#' @field errors (`character()`)\cr
#' Logged errors as vector.
errors = function(rhs) {
assert_ro_binding(rhs)
get_log_condition(self$state, "error")
},
#' @template field_hash
hash = function(rhs) {
assert_ro_binding(rhs)
calculate_hash(class(self), self$id, self$param_set$values, private$.predict_type,
self$fallback$hash, self$parallel_predict, get0("validate", self))
},
#' @field phash (`character(1)`)\cr
#' Hash (unique identifier) for this partial object, excluding some components
#' which are varied systematically during tuning (parameter values).
phash = function(rhs) {
assert_ro_binding(rhs)
calculate_hash(class(self), self$id, private$.predict_type,
self$fallback$hash, self$parallel_predict, get0("validate", self))
},
#' @field predict_type (`character(1)`)\cr
#' Stores the currently active predict type, e.g. `"response"`.
#' Must be an element of `$predict_types`.
predict_type = function(rhs) {
if (missing(rhs)) {
return(private$.predict_type)
}
assert_string(rhs, .var.name = "predict_type")
if (rhs %nin% self$predict_types) {
stopf("Learner '%s' does not support predict type '%s'", self$id, rhs)
}
private$.predict_type = rhs
},
#' @template field_param_set
param_set = function(rhs) {
if (!missing(rhs) && !identical(rhs, private$.param_set)) {
stop("param_set is read-only.")
}
private$.param_set
},
#' @field encapsulate (named `character()`)\cr
#' Controls how to execute the code in internal train and predict methods.
#' Must be a named character vector with names `"train"` and `"predict"`.
#' Possible values are `"none"`, `"try"`, `"evaluate"` (requires package \CRANpkg{evaluate}) and `"callr"` (requires package \CRANpkg{callr}).
#' See [mlr3misc::encapsulate()] for more details.
encapsulate = function(rhs) {
default = c(train = "none", predict = "none")
if (missing(rhs)) {
return(insert_named(default, private$.encapsulate))
}
assert_character(rhs)
assert_names(names(rhs), subset.of = c("train", "predict"))
private$.encapsulate = insert_named(default, rhs)
},
#' @field fallback ([Learner])\cr
#' Learner which is fitted to impute predictions in case that either the model fitting or the prediction of the top learner is not successful.
#' Requires encapsulation, otherwise errors are not caught and the execution is terminated before the fallback learner kicks in.
#' If you have not set encapsulation manually before, setting the fallback learner automatically
#' activates encapsulation using the \CRANpkg{evaluate} package.
#' Also see the section on error handling the mlr3book:
#' \url{https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-error-handling}
fallback = function(rhs) {
if (missing(rhs)) {
return(private$.fallback)
}
if (!is.null(rhs)) {
assert_learner(rhs, task_type = self$task_type)
if (!identical(self$predict_type, rhs$predict_type)) {
warningf("The fallback learner '%s' and the base learner '%s' have different predict types: '%s' != '%s'.",
rhs$id, self$id, rhs$predict_type, self$predict_type)
}
if (is.null(private$.encapsulate)) {
private$.encapsulate = c(train = "evaluate", predict = "evaluate")
}
}
private$.fallback = rhs
},
#' @field hotstart_stack ([HotstartStack])\cr.
#' Stores `HotstartStack`.
hotstart_stack = function(rhs) {
if (missing(rhs)) {
return(private$.hotstart_stack)
}
assert_r6(rhs, "HotstartStack", null.ok = TRUE)
private$.hotstart_stack = rhs
}
),
private = list(
.encapsulate = NULL,
.fallback = NULL,
.predict_type = NULL,
.param_set = NULL,
.hotstart_stack = NULL,
deep_clone = function(name, value) {
switch(name,
.param_set = value$clone(deep = TRUE),
.fallback = if (is.null(value)) NULL else value$clone(deep = TRUE),
state = {
if (!is.null(value$train_task)) {
value$train_task = value$train_task$clone(deep = TRUE)
}
value$log = copy(value$log)
value
},
value
)
}
)
)
#' @export
rd_info.Learner = function(obj, ...) {
x = c("",
sprintf("* Task type: %s", rd_format_string(obj$task_type)),
sprintf("* Predict Types: %s", rd_format_string(obj$predict_types)),
sprintf("* Feature Types: %s", rd_format_string(obj$feature_types)),
sprintf("* Required Packages: %s", rd_format_packages(obj$packages))
)
paste(x, collapse = "\n")
}
get_log_condition = function(state, condition) {
if (is.null(state$log)) {
character()
} else {
fget(state$log, i = condition, j = "msg", key = "class")
}
}
#' @export
default_values.Learner = function(x, search_space, task, ...) { # nolint
values = default_values(x$param_set)
if (any(search_space$ids() %nin% names(values))) {
stopf("Could not find default values for the following parameters: %s",
str_collapse(setdiff(search_space$ids(), names(values))))
}
values[search_space$ids()]
}
# #' @export
# format_list_item.Learner = function(x, ...) { # nolint
# sprintf("<lrn:%s>", x$id)
# }
#' @export
marshal_model.learner_state = function(model, inplace = FALSE, ...) {
if (is.null(model$model)) {
return(model)
}
mm = marshal_model(model$model, inplace = inplace, ...)
if (!is_marshaled_model(mm)) {
return(model)
}
model$model = mm
structure(list(
marshaled = model,
packages = "mlr3"
), class = c("learner_state_marshaled", "list_marshaled", "marshaled"))
}
#' @export
unmarshal_model.learner_state_marshaled = function(model, inplace = FALSE, ...) {
mm = model$marshaled
mm$model = unmarshal_model(mm$model, inplace = inplace, ...)
return(mm)
}
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