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refactor: pass extra information of the result in the extra parameter (#458)
* refactor: pass extra information of the result in the extra parameter * ... * ...
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‎DESCRIPTION

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@@ -29,7 +29,7 @@ Depends:
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paradox (>= 1.0.1),
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R (>= 3.1.0)
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Imports:
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bbotk (>= 1.1.1),
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bbotk (>= 1.2.0),
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checkmate (>= 2.0.0),
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data.table,
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lgr,

‎NEWS.md

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# mlr3tuning (development version)
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* fix: The `as_data_table()` functions do not unnest the `x_domain` colum anymore by default.
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* fix: `to_tune(internal = TRUE)` now also works if non-internal tuning parameters require have
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an `.extra_trafo`
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* fix: `to_tune(internal = TRUE)` now also works if non-internal tuning parameters require have an `.extra_trafo`.
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* feat: It is now possible to pass an `internal_search_space` manually.
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This allows to use parameter transformations on the primary search space in combination with
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internal hyperparameter tuning.
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This allows to use parameter transformations on the primary search space in combination with internal hyperparameter tuning.
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* refactor: The `Tuner` pass extra information of the result in the `extra` parameter now.
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# mlr3tuning 1.0.2
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‎R/TuningInstanceAsyncMulticrit.R

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#' @template param_xdt
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#' @template param_learner_param_vals
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#' @template param_internal_tuned_values
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#' @template param_extra
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#'
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#' @template field_internal_search_space
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#'
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#' For internal use.
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#'
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#' @param ydt (`numeric(1)`)\cr
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#' Optimal outcomes, e.g. the Pareto front.
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#' Optimal outcomes, e.g. the Pareto front.
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#' @param xydt (`data.table::data.table()`)\cr
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#' Point, outcome, and additional information.
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assign_result = function(xdt, ydt, learner_param_vals = NULL, xydt = NULL) {
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#' Point, outcome, and additional information.
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#' @param ... (`any`)\cr
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#' ignored.
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assign_result = function(xdt, ydt, learner_param_vals = NULL, extra = NULL, xydt = NULL, ...) {
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# workaround
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extra = extra %??% xydt
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# extract internal tuned values
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if ("internal_tuned_values" %in% names(xydt)) {
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set(xdt, j = "internal_tuned_values", value = list(xydt[["internal_tuned_values"]]))
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if ("internal_tuned_values" %in% names(extra)) {
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set(xdt, j = "internal_tuned_values", value = list(extra[["internal_tuned_values"]]))
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}
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# set the column with the learner param_vals that were not optimized over but set implicitly

‎R/TuningInstanceAsyncSingleCrit.R

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#' @template param_xdt
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#' @template param_learner_param_vals
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#' @template param_internal_tuned_values
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#' @template param_extra
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#'
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#' @template field_internal_search_space
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#'
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#' @param y (`numeric(1)`)\cr
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#' Optimal outcome.
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#' @param xydt (`data.table::data.table()`)\cr
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#' Point, outcome, and additional information.
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assign_result = function(xdt, y, learner_param_vals = NULL, xydt = NULL) {
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#' Point, outcome, and additional information (Deprecated).
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#' @param ... (`any`)\cr
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#' ignored.
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assign_result = function(xdt, y, learner_param_vals = NULL, extra = NULL, xydt = NULL, ...) {
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# workaround
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extra = extra %??% xydt
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# set the column with the learner param_vals that were not optimized over but set implicitly
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assert_list(learner_param_vals, null.ok = TRUE, names = "named")
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# extract internal tuned values
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if ("internal_tuned_values" %in% names(xydt)) {
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set(xdt, j = "internal_tuned_values", value = list(xydt[["internal_tuned_values"]]))
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if ("internal_tuned_values" %in% names(extra)) {
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set(xdt, j = "internal_tuned_values", value = list(extra[["internal_tuned_values"]]))
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}
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if (is.null(learner_param_vals)) {

‎R/TuningInstanceBatchMulticrit.R

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#' @template param_xdt
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#' @template param_learner_param_vals
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#' @template param_internal_tuned_values
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#' @template param_extra
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#'
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#' @template field_internal_search_space
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#'
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#' For internal use.
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#'
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#' @param ydt (`data.table::data.table()`)\cr
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#' Optimal outcomes, e.g. the Pareto front.
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#' Optimal outcomes, e.g. the Pareto front.
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#' @param xydt (`data.table::data.table()`)\cr
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#' Point, outcome, and additional information.
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assign_result = function(xdt, ydt, learner_param_vals = NULL, xydt = NULL) {
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#' Point, outcome, and additional information (Deprecated).
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#' @param ... (`any`)\cr
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#' ignored.
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assign_result = function(xdt, ydt, learner_param_vals = NULL, extra = NULL, xydt = NULL, ...) {
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# workaround
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extra = extra %??% xydt
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# extract internal tuned values
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if ("internal_tuned_values" %in% names(xydt)) {
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set(xdt, j = "internal_tuned_values", value = list(xydt[["internal_tuned_values"]]))
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if ("internal_tuned_values" %in% names(extra)) {
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set(xdt, j = "internal_tuned_values", value = list(extra[["internal_tuned_values"]]))
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}
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# set the column with the learner param_vals that were not optimized over but set implicitly

‎R/TuningInstanceBatchSingleCrit.R

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#' @template param_xdt
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#' @template param_learner_param_vals
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#' @template param_internal_tuned_values
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#' @template param_extra
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#'
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#' @template field_internal_search_space
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#'
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#' For internal use.
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#'
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#' @param y (`numeric(1)`)\cr
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#' Optimal outcome.
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#' Optimal outcome.
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#' @param xydt (`data.table::data.table()`)\cr
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#' Point, outcome, and additional information.
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assign_result = function(xdt, y, learner_param_vals = NULL, xydt = NULL) {
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#' Point, outcome, and additional information (Deprecated).
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#' @param ... (`any`)\cr
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#' ignored.
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assign_result = function(xdt, y, learner_param_vals = NULL, extra = NULL, xydt = NULL, ...) {
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# workaround
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extra = extra %??% xydt
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# set the column with the learner param_vals that were not optimized over but set implicitly
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assert_list(learner_param_vals, null.ok = TRUE, names = "named")
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# extract internal tuned values
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if ("internal_tuned_values" %in% names(xydt)) {
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set(xdt, j = "internal_tuned_values", value = list(xydt[["internal_tuned_values"]]))
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if ("internal_tuned_values" %in% names(extra)) {
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set(xdt, j = "internal_tuned_values", value = list(extra[["internal_tuned_values"]]))
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}
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# learner param values

‎man-roxygen/param_extra.R

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#' @param extra (`data.table::data.table()`)\cr
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#' Additional information.

‎man/TuningInstanceAsyncMultiCrit.Rd

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‎man/TuningInstanceAsyncSingleCrit.Rd

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‎man/TuningInstanceBatchMultiCrit.Rd

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