Update README and remove x_domain from result

Author: be-marc

R/FSelectInstanceMultiCrit.R

@@ -95,7 +95,11 @@ FSelectInstanceMultiCrit = R6Class("FSelectInstanceMultiCrit",
         self$objective$task$feature_names[as.logical(x)]
       })
       xdt[, features := list(features)]
-      super$assign_result(xdt, ydt)
+      assert_data_table(xdt)
+      assert_names(names(xdt), must.include = self$search_space$ids())
+      assert_data_table(ydt)
+      assert_names(names(ydt), permutation.of = self$objective$codomain$ids())
+      private$.result = cbind(xdt, ydt)
     }
   ),
 

R/FSelectInstanceSingleCrit.R

@@ -93,7 +93,11 @@ FSelectInstanceSingleCrit = R6Class("FSelectInstanceSingleCrit",
       # Add feature names to result for easy task subsetting
       features = list(self$objective$task$feature_names[as.logical(xdt)])
       xdt[, features := list(features)]
-      super$assign_result(xdt, y)
+      assert_data_table(xdt, nrows = 1L)
+      assert_names(names(xdt), must.include = self$search_space$ids())
+      assert_number(y)
+      assert_names(names(y), permutation.of = self$objective$codomain$ids())
+      private$.result = cbind(xdt, t(y)) # t(y) so the name of y stays
     }
   ),
 

README.Rmd

@@ -0,0 +1,78 @@
+---
+output: github_document
+---
+
+```{r, include = FALSE}
+lgr::get_logger("mlr3")$set_threshold("warn")
+lgr::get_logger("bbotk")$set_threshold("warn")
+set.seed(1)
+options(datatable.print.class = FALSE, datatable.print.keys = FALSE)
+```
+
+# mlr3fselect
+
+Package website: [release](https://mlr3fselect.mlr-org.com/) | [dev](https://mlr3fselect.mlr-org.com/dev/)
+
+<!-- badges: start -->
+[![tic](https://github.com/mlr-org/mlr3fselect/workflows/tic/badge.svg?branch=main)](https://github.com/mlr-org/mlr3fselect/actions)
+[![CRAN Status](https://www.r-pkg.org/badges/version/mlr3fselect)](https://cran.r-project.org/package=mlr3fselect)
+[![StackOverflow](https://img.shields.io/badge/stackoverflow-mlr3-orange.svg)](https://stackoverflow.com/questions/tagged/mlr3)
+[![Mattermost](https://img.shields.io/badge/chat-mattermost-orange.svg)](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
+[![CodeFactor](https://www.codefactor.io/repository/github/mlr-org/mlr3fselect/badge)](https://www.codefactor.io/repository/github/mlr-org/mlr3fselect)
+<!-- badges: end -->
+
+This package provides feature selection for [mlr3](https://mlr3.mlr-org.com).
+It offers various feature selection wrappers, e.g. random search and sequential feature selection and different termination criteria can be set and combined.'AutoFSelect' provides a convenient way to perform nested resampling in combination with 'mlr3'.
+The package is build on [bbotk](https://github.com/mlr-org/bbotk) which provides a common framework for optimization.
+For feature filters and embedded methods, see [mlr3filters](https://mlr3filters.mlr-org.com)
+
+## Resources
+
+* mlr3book [chapter](https://mlr3book.mlr-org.com/fs.html)
+* mlr3gallery [post](https://mlr3gallery.mlr-org.com/posts/2020-09-14-mlr3fselect-basic/)
+* [cheatsheet](https://cheatsheets.mlr-org.com/mlr3fselect.pdf) 
+
+## Installation
+
+Install the last release from CRAN:
+
+```{r eval = FALSE}
+install.packages("mlr3fselect")
+```
+
+Install the development version from GitHub:
+
+```{r eval = FALSE}
+remotes::install_github("mlr-org/mlr3fselect")
+```
+
+## Example
+
+```{r}
+library("mlr3")
+library("mlr3fselect")
+
+task = tsk("pima")
+learner = lrn("classif.rpart")
+resampling = rsmp("holdout")
+measure = msr("classif.ce")
+
+# define termination criterion
+terminator = trm("evals", n_evals = 20)
+
+# create fselect instance
+instance = FSelectInstanceSingleCrit$new(
+  task = task,
+  learner = learner,
+  resampling = resampling,
+  measure = measure,
+  terminator = terminator)
+
+# load fselector
+fselector = fs("random_search")
+
+# trigger optimization
+fselector$optimize(instance)
+```
+
+

README.md

@@ -1,41 +1,53 @@
+
 # mlr3fselect
 
-Package website: [release](https://mlr3fselect.mlr-org.com/) | [dev](https://mlr3fselect.mlr-org.com/dev/)
+Package website: [release](https://mlr3fselect.mlr-org.com/) |
+[dev](https://mlr3fselect.mlr-org.com/dev/)
 
 <!-- badges: start -->
+
 [![tic](https://github.com/mlr-org/mlr3fselect/workflows/tic/badge.svg?branch=main)](https://github.com/mlr-org/mlr3fselect/actions)
-[![CRAN Status](https://www.r-pkg.org/badges/version/mlr3fselect)](https://cran.r-project.org/package=mlr3fselect)
+[![CRAN
+Status](https://www.r-pkg.org/badges/version/mlr3fselect)](https://cran.r-project.org/package=mlr3fselect)
 [![StackOverflow](https://img.shields.io/badge/stackoverflow-mlr3-orange.svg)](https://stackoverflow.com/questions/tagged/mlr3)
 [![Mattermost](https://img.shields.io/badge/chat-mattermost-orange.svg)](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
 [![CodeFactor](https://www.codefactor.io/repository/github/mlr-org/mlr3fselect/badge)](https://www.codefactor.io/repository/github/mlr-org/mlr3fselect)
 <!-- badges: end -->
 
-This package provides feature selection for [mlr3](https://mlr3.mlr-org.com).
-It offers various feature selection wrappers, e.g. random search and sequential feature selection and
-different termination criteria can be set and combined. 'AutoFSelect' provides a
-convenient way to perform nested resampling in combination with 'mlr3'. The
-package is build on [bbotk](https://github.com/mlr-org/bbotk) which provides a
-common framework for optimization.
+This package provides feature selection for
+[mlr3](https://mlr3.mlr-org.com). It offers various feature selection
+wrappers, e.g. random search and sequential feature selection and
+different termination criteria can be set and combined.’AutoFSelect’
+provides a convenient way to perform nested resampling in combination
+with ‘mlr3’. The package is build on
+[bbotk](https://github.com/mlr-org/bbotk) which provides a common
+framework for optimization. For feature filters and embedded methods,
+see [mlr3filters](https://mlr3filters.mlr-org.com)
+
+## Resources
 
-For feature filters and embedded methods, see [mlr3filters](https://mlr3filters.mlr-org.com)
+  - mlr3book [chapter](https://mlr3book.mlr-org.com/fs.html)
+  - mlr3gallery
+    [post](https://mlr3gallery.mlr-org.com/posts/2020-09-14-mlr3fselect-basic/)
+  - [cheatsheet](https://cheatsheets.mlr-org.com/mlr3fselect.pdf)
 
 ## Installation
 
-CRAN version
+Install the last release from CRAN:
 
-```r
+``` r
 install.packages("mlr3fselect")
 ```
 
-Development version
+Install the development version from GitHub:
 
-```r
+``` r
 remotes::install_github("mlr-org/mlr3fselect")
 ```
 
 ## Example
 
-```r
+``` r
 library("mlr3")
 library("mlr3fselect")
 
@@ -44,24 +56,25 @@ learner = lrn("classif.rpart")
 resampling = rsmp("holdout")
 measure = msr("classif.ce")
 
-# Define termination criterion
+# define termination criterion
 terminator = trm("evals", n_evals = 20)
 
-# Create fselect instance
-instance = FSelectInstanceSingleCrit$new(task = task,
+# create fselect instance
+instance = FSelectInstanceSingleCrit$new(
+  task = task,
   learner = learner,
   resampling = resampling,
   measure = measure,
   terminator = terminator)
 
-# Load fselector
+# load fselector
 fselector = fs("random_search")
 
-# Trigger optimization
+# trigger optimization
 fselector$optimize(instance)
-
-# View results
-instance$result
 ```
 
-
+    ##     age glucose insulin mass pedigree pregnant pressure triceps
+    ## 1: TRUE    TRUE    TRUE TRUE     TRUE    FALSE    FALSE    TRUE
+    ##                                     features classif.ce
+    ## 1: age,glucose,insulin,mass,pedigree,triceps  0.1757812

man/AutoFSelector.Rd

@@ -67,7 +67,7 @@ test_fselector = function(.key, ..., term_evals = 2L, real_evals = term_evals,
 
   fselector = fs(.key, ...)
   expect_fselector(fselector)
-  expect_data_table(fselector$optimize(inst), nrows = 1, ncols = 7,
+  expect_data_table(fselector$optimize(inst), nrows = 1, ncols = 6,
     any.missing = FALSE)
   archive = inst$archive
 
@@ -76,21 +76,15 @@ test_fselector = function(.key, ..., term_evals = 2L, real_evals = term_evals,
   expect_equal(inst$archive$n_evals, real_evals)
 
   # Result checks
-  expect_data_table(inst$result, nrows = 1, ncols = 7)
+  expect_data_table(inst$result, nrows = 1, ncols = 6)
   expect_named(inst$result, c(
     "x1",
     "x2",
     "x3",
     "x4",
     "features",
-    "x_domain",
     "dummy"))
   expect_character(inst$result$features[[1]])
-  expect_named(inst$result_x_domain, c(
-    "x1",
-    "x2",
-    "x3",
-    "x4"))
   expect_data_table(inst$result_x_search_space, nrows = 1, ncols = 4,
     types = "logical")
   expect_named(inst$result_x_search_space, c(
@@ -116,7 +110,7 @@ test_fselector_2D = function(.key, ..., term_evals = 2L, real_evals = term_evals
 
   fselector = fs(.key, ...)
   expect_fselector(fselector)
-  expect_data_table(fselector$optimize(inst), ncols = 8,
+  expect_data_table(fselector$optimize(inst), ncols = 7,
     any.missing = FALSE)
   archive = inst$archive
 
@@ -125,23 +119,16 @@ test_fselector_2D = function(.key, ..., term_evals = 2L, real_evals = term_evals
   expect_equal(inst$archive$n_evals, real_evals)
 
   # Result checks
-  expect_data_table(inst$result, ncols = 8)
+  expect_data_table(inst$result, ncols = 7)
   expect_named(inst$result, c(
     "x1",
     "x2",
     "x3",
     "x4",
     "features",
-    "x_domain",
     "regr.rmse",
     "regr.mse"))
   expect_character(inst$result$features[[1]])
-
-  expect_named(inst$result_x_domain[[1]], c(
-    "x1",
-    "x2",
-    "x3",
-    "x4"))
   expect_data_table(inst$result_x_search_space, ncols = 4,
     types = "logical")
   expect_named(inst$result_x_search_space, c(