refactor: prepare for bbotk 0.9.0

DESCRIPTION

@@ -25,12 +25,12 @@ Depends:
     mlr3 (>= 0.12.0),
     R (>= 3.1.0)
 Imports:
-    bbotk (>= 0.7.2),
+    bbotk (>= 0.8.0.9000),
     checkmate (>= 2.0.0),
     data.table,
     lgr,
-    mlr3misc (>= 0.9.4),
-    paradox (>= 0.7.0),
+    mlr3misc (>= 0.15.0.9000),
+    paradox (>= 1.0.0),
     R6
 Suggests:
     e1071,
@@ -39,6 +39,10 @@ Suggests:
     mlr3pipelines,
     rpart,
     testthat (>= 3.0.0)
+Remotes:
+    mlr-org/bbotk,
+    mlr-org/paradox,
+    mlr-org/mlr3misc
 Config/testthat/edition: 3
 Config/testthat/parallel: true
 Encoding: UTF-8
@@ -47,24 +51,26 @@ NeedsCompilation: no
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.1
 Collate:
-    'ArchiveFSelect.R'
+    'ArchiveBatchFSelect.R'
     'AutoFSelector.R'
-    'CallbackFSelect.R'
-    'ContextEval.R'
-    'FSelectInstanceSingleCrit.R'
-    'FSelectInstanceMultiCrit.R'
+    'CallbackBatchFSelect.R'
+    'ContextBatchFSelect.R'
+    'FSelectInstanceBatchSingleCrit.R'
+    'FSelectInstanceBatchMultiCrit.R'
     'mlr_fselectors.R'
     'FSelector.R'
-    'FSelectorDesignPoints.R'
-    'FSelectorExhaustiveSearch.R'
-    'FSelectorFromOptimizer.R'
-    'FSelectorGeneticSearch.R'
-    'FSelectorRFE.R'
-    'FSelectorRFECV.R'
-    'FSelectorRandomSearch.R'
-    'FSelectorSequential.R'
-    'FSelectorShadowVariableSearch.R'
+    'FSelectorBatch.R'
+    'FSelectorBatchDesignPoints.R'
+    'FSelectorBatchExhaustiveSearch.R'
+    'FSelectorBatchFromOptimizerBatch.R'
+    'FSelectorBatchGeneticSearch.R'
+    'FSelectorBatchRFE.R'
+    'FSelectorBatchRFECV.R'
+    'FSelectorBatchRandomSearch.R'
+    'FSelectorBatchSequential.R'
+    'FSelectorBatchShadowVariableSearch.R'
     'ObjectiveFSelect.R'
+    'ObjectiveFSelectBatch.R'
     'assertions.R'
     'auto_fselector.R'
     'bibentries.R'

NAMESPACE

@@ -1,29 +1,37 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(as.data.table,ArchiveFSelect)
+S3method(as.data.table,ArchiveBatchFSelect)
 S3method(as.data.table,DictionaryFSelector)
 S3method(extract_inner_fselect_archives,BenchmarkResult)
 S3method(extract_inner_fselect_archives,ResampleResult)
 S3method(extract_inner_fselect_results,BenchmarkResult)
 S3method(extract_inner_fselect_results,ResampleResult)
-export(ArchiveFSelect)
+export(ArchiveBatchFSelect)
 export(AutoFSelector)
-export(ContextEval)
-export(FSelectInstanceMultiCrit)
-export(FSelectInstanceSingleCrit)
+export(ContextBatchFSelect)
+export(FSelectInstanceBatchMultiCrit)
+export(FSelectInstanceBatchSingleCrit)
 export(FSelector)
-export(FSelectorDesignPoints)
-export(FSelectorExhaustiveSearch)
-export(FSelectorFromOptimizer)
-export(FSelectorGeneticSearch)
-export(FSelectorRFE)
-export(FSelectorRFECV)
-export(FSelectorRandomSearch)
-export(FSelectorSequential)
-export(FSelectorShadowVariableSearch)
+export(FSelectorBatch)
+export(FSelectorBatchDesignPoints)
+export(FSelectorBatchExhaustiveSearch)
+export(FSelectorBatchFromOptimizerBatch)
+export(FSelectorBatchGeneticSearch)
+export(FSelectorBatchRFE)
+export(FSelectorBatchRFECV)
+export(FSelectorBatchRandomSearch)
+export(FSelectorBatchSequential)
+export(FSelectorBatchShadowVariableSearch)
 export(ObjectiveFSelect)
+export(ObjectiveFSelectBatch)
+export(assert_fselect_instance)
+export(assert_fselect_instance_async)
+export(assert_fselect_instance_batch)
+export(assert_fselector_async)
+export(assert_fselector_batch)
+export(assert_fselectors)
 export(auto_fselector)
-export(callback_fselect)
+export(callback_batch_fselect)
 export(clbk)
 export(clbks)
 export(extract_inner_fselect_archives)

NEWS.md

@@ -3,15 +3,15 @@
 # mlr3fselect 0.12.0
 
 * feat: Add number of features to `instance$result`.
-* feat: Add `ties_method` options `"least_features"` and `"random"` to `ArchiveFSelect$best()`.
-* refactor: Optimize runtime of `ArchiveFSelect$best()` method.
+* feat: Add `ties_method` options `"least_features"` and `"random"` to `ArchiveBatchFSelect$best()`.
+* refactor: Optimize runtime of `ArchiveBatchFSelect$best()` method.
 * feat: Add importance scores to result of `FSelectorRFE`.
-* feat: Add number of features to `as.data.table.ArchiveFSelect()`.
+* feat: Add number of features to `as.data.table.ArchiveBatchFSelect()`.
 * feat: Features can be always included with the `always_include` column role.
 * fix: Add `$phash()` method to `AutoFSelector`.
 * fix: Include `FSelector` in hash of  `AutoFSelector`.
-* refactor: Change default batch size of `FSelectorRandomSearch` to 10.
-* feat: Add `batch_size` parameter to `FSelectorExhaustiveSearch` to reduce memory consumption.
+* refactor: Change default batch size of `FSelectorBatchRandomSearch` to 10.
+* feat: Add `batch_size` parameter to `FSelectorBatchExhaustiveSearch` to reduce memory consumption.
 * compatibility: Work with new paradox version 1.0.0
 
 # mlr3fselect 0.11.0
@@ -29,7 +29,7 @@
 * refactor: The importance scores in `FSelectorRFE` are now aggregated by rank instead of averaging them.
 * feat: Add `FSelectorRFECV` optimizer to run recursive feature elimination with cross-validation.
 * refactor: `FSelectorRFE` works without `store_models = TRUE` now.
-* feat: The `as.data.table.ArchiveFSelect()` function additionally returns a character vector of selected features for each row.
+* feat: The `as.data.table.ArchiveBatchFSelect()` function additionally returns a character vector of selected features for each row.
 * refactor: Add `callbacks` argument to `fsi()` function.
 
 # mlr3fselect 0.9.1
@@ -39,18 +39,18 @@
 
 # mlr3fselect 0.9.0
 
-* fix: Add `genalg` to required packages of `FSelectorGeneticSearch`.
+* fix: Add `genalg` to required packages of `FSelectorBatchGeneticSearch`.
 * feat: Add new callback that backups the benchmark result to disk after each batch.
-* feat: Create custom callbacks with the `callback_fselect()` function.
+* feat: Create custom callbacks with the `callback_batch_fselect()` function.
 
 # mlr3fselect 0.8.0
 
 * refactor: `FSelectorRFE` throws an error if the learner does not support the `$importance()` method.
 * refactor: The `AutoFSelector` stores the instance and benchmark result if `store_models = TRUE`.
 * refactor: The `AutoFSelector` stores the instance if `store_benchmark_result = TRUE`.
 * feat: Add missing parameters from `AutoFSelector` to `auto_fselect()`.
-* feat: Add `fsi()` function to create a `FSelectInstanceSingleCrit` or `FSelectInstanceMultiCrit`.
-* refactor: Remove `unnest` option from `as.data.table.ArchiveFSelect()` function.
+* feat: Add `fsi()` function to create a `FSelectInstanceBatchSingleCrit` or `FSelectInstanceBatchMultiCrit`.
+* refactor: Remove `unnest` option from `as.data.table.ArchiveBatchFSelect()` function.
 
 # mlr3fselect 0.7.2
 
@@ -67,7 +67,7 @@
 * docs: New examples with `fselect()` function.
 * feat: `$help()` method which opens manual page of a `FSelector`.
 * feat: Added a `as.data.table.DictionaryFSelector` function.
-* feat: Added `min_features` parameter to `FSelectorSequential`.
+* feat: Added `min_features` parameter to `FSelectorBatchSequential`.
 
 # mlr3fselect 0.6.1
 
@@ -90,7 +90,7 @@
 
 * `FSelectorRFE` stores importance values of each evaluated feature set in
   archive.
-* `ArchiveFSelect$data` is a public field now.
+* `ArchiveBatchFSelect$data` is a public field now.
 
 # mlr3fselect 0.4.1
 
@@ -110,15 +110,15 @@
 * Option to control `store_benchmark_result`, `store_models` and `check_values`
   in `AutoFSelector`. `store_fselect_instance` must be set as a parameter during
   initialization.
-* Adds `FSelectorGeneticSearch`.
-* Fixes `check_values` flag in `FSelectInstanceSingleCrit` and
-  `FSelectInstanceMultiCrit`.
+* Adds `FSelectorBatchGeneticSearch`.
+* Fixes `check_values` flag in `FSelectInstanceBatchSingleCrit` and
+  `FSelectInstanceBatchMultiCrit`.
 * Removed dependency on orphaned package `bibtex`.
 * `PipeOpSelect` is internally used for task subsetting.
 
 # mlr3fselect 0.3.0
 
-* `Archive` is `ArchiveFSelect` now which stores the benchmark result in
+* `Archive` is `ArchiveBatchFSelect` now which stores the benchmark result in
   `$benchmark_result`. This change removed the resample results from the archive
   but they can be still accessed via the benchmark result.
 

R/ArchiveFSelect.R → R/ArchiveBatchFSelect.R

@@ -1,10 +1,10 @@
 #' @title Class for Logging Evaluated Feature Sets
 #'
 #' @description
-#' The [ArchiveFSelect] stores all evaluated feature sets and performance scores.
+#' The [ArchiveBatchFSelect] stores all evaluated feature sets and performance scores.
 #'
 #' @details
-#' The [ArchiveFSelect] is a container around a [data.table::data.table()].
+#' The [ArchiveBatchFSelect] is a container around a [data.table::data.table()].
 #' Each row corresponds to a single evaluation of a feature set.
 #' See the section on Data Structure for more information.
 #' The archive stores additionally a [mlr3::BenchmarkResult] (`$benchmark_result`) that records the resampling experiments.
@@ -40,10 +40,10 @@
 #' Alternatively, measures can be supplied to `as.data.table()`.
 #'
 #' @section S3 Methods:
-#' * `as.data.table.ArchiveFSelect(x, exclude_columns = "uhash", measures = NULL)`\cr
+#' * `as.data.table.ArchiveBatchFSelect(x, exclude_columns = "uhash", measures = NULL)`\cr
 #' Returns a tabular view of all evaluated feature sets.\cr
-#' [ArchiveFSelect] -> [data.table::data.table()]\cr
-#'     * `x` ([ArchiveFSelect])
+#' [ArchiveBatchFSelect] -> [data.table::data.table()]\cr
+#'     * `x` ([ArchiveBatchFSelect])
 #'     * `exclude_columns` (`character()`)\cr
 #'       Exclude columns from table. Set to `NULL` if no column should be excluded.
 #'     * `measures` (list of [mlr3::Measure])\cr
@@ -52,8 +52,8 @@
 #' @template param_ties_method
 #'
 #' @export
-ArchiveFSelect = R6Class("ArchiveFSelect",
-  inherit = Archive,
+ArchiveBatchFSelect = R6Class("ArchiveBatchFSelect",
+  inherit = ArchiveBatch,
 
   public = list(
 
@@ -212,7 +212,7 @@ ArchiveFSelect = R6Class("ArchiveFSelect",
 )
 
 #' @export
-as.data.table.ArchiveFSelect = function(x, ..., exclude_columns = "uhash", measures = NULL) {
+as.data.table.ArchiveBatchFSelect = function(x, ..., exclude_columns = "uhash", measures = NULL) {
   if (nrow(x$data) == 0) return(data.table())
   # always ignore x_domain column
   exclude_columns = c("x_domain", exclude_columns)

R/AutoFSelector.R

@@ -100,7 +100,7 @@ AutoFSelector = R6Class("AutoFSelector",
   public = list(
 
     #' @field instance_args (`list()`)\cr
-    #' All arguments from construction to create the [FSelectInstanceSingleCrit].
+    #' All arguments from construction to create the [FSelectInstanceBatchSingleCrit].
     instance_args = NULL,
 
     #' @field fselector ([FSelector])\cr
@@ -122,7 +122,7 @@ AutoFSelector = R6Class("AutoFSelector",
       store_benchmark_result = TRUE,
       store_models = FALSE,
       check_values = FALSE,
-      callbacks = list(),
+      callbacks = NULL,
       ties_method = "least_features"
       ) {
       ia = list()
@@ -249,8 +249,8 @@ AutoFSelector = R6Class("AutoFSelector",
 
   active = list(
 
-    #' @field archive ([ArchiveFSelect)\cr
-    #' Returns [FSelectInstanceSingleCrit] archive.
+    #' @field archive ([ArchiveBatchFSelect)\cr
+    #' Returns [FSelectInstanceBatchSingleCrit] archive.
     archive = function() self$fselect_instance$archive,
 
     #' @field learner ([mlr3::Learner])\cr
@@ -264,12 +264,12 @@ AutoFSelector = R6Class("AutoFSelector",
       }
     },
 
-    #' @field fselect_instance ([FSelectInstanceSingleCrit])\cr
+    #' @field fselect_instance ([FSelectInstanceBatchSingleCrit])\cr
     #' Internally created feature selection instance with all intermediate results.
     fselect_instance = function() self$model$fselect_instance,
 
     #' @field fselect_result ([data.table::data.table])\cr
-    #' Short-cut to `$result` from [FSelectInstanceSingleCrit].
+    #' Short-cut to `$result` from [FSelectInstanceBatchSingleCrit].
     fselect_result = function() self$fselect_instance$result,
 
     #' @field predict_type (`character(1)`)\cr
@@ -311,7 +311,7 @@ AutoFSelector = R6Class("AutoFSelector",
       # construct instance from args; then tune
       ia = self$instance_args
       ia$task = task$clone()
-      instance = invoke(FSelectInstanceSingleCrit$new, .args = ia)
+      instance = invoke(FSelectInstanceBatchSingleCrit$new, .args = ia)
       self$fselector$optimize(instance)
       learner = ia$learner$clone(deep = TRUE)
       task = task$clone()