cb_show + type refactor (#157)

* allow show_cb argument to be passed on to autoplot.precrec * refactor: assert_choice for type of plots * update docs * add one test for getting hint of available typet from an autoplot * update news
mlr-org · Nov 15, 2024 · d876a9f · d876a9f
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diff --git a/NEWS.md b/NEWS.md
@@ -1,5 +1,9 @@
 # mlr3viz (development version)
 
+- Allow passing parameters to `precrec::autoplot()` (eg `show_cb`) when plotting 
+`BenchmarkResult` and `ResampleResult` objects, using `type` = `roc` or `prc`.
+- Refactor: wrong `type` in `autoplot`s now gives hints of which ones to use.
+
 # mlr3viz 0.10.0
 
 - Add plot for `LearnerSurvCoxPH`.

diff --git a/R/BenchmarkResult.R b/R/BenchmarkResult.R
@@ -17,7 +17,8 @@
 #' @template param_type
 #' @template param_measure
 #' @template param_theme
-#' @param ... (ignored).
+#' @param ... arguments passed on to [precrec::autoplot()] for `type = "roc"` or `"prc"`.
+#' Useful to e.g. remove confidence bands with `show_cb = FALSE`.
 #'
 #' @return [ggplot2::ggplot()].
 #'
@@ -41,7 +42,7 @@
 #'   autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
 #' }
 autoplot.BenchmarkResult = function(object, type = "boxplot", measure = NULL, theme = theme_minimal(), ...) {
-  assert_string(type)
+  assert_choice(type, choices = c("boxplot", "roc", "prc"), null.ok = FALSE)
 
   task = object$tasks$task[[1L]]
   measure = mlr3::assert_measure(mlr3::as_measure(measure, task_type = task$task_type), task = task)
@@ -74,7 +75,7 @@ autoplot.BenchmarkResult = function(object, type = "boxplot", measure = NULL, th
     },
 
     "roc" = {
-      p = plot_precrec(object, curvetype = "ROC")
+      p = plot_precrec(object, curvetype = "ROC", ...)
       p$layers[[1]]$mapping = aes(color = modname, fill = modname)
       # fill confidence bounds
       p +
@@ -84,7 +85,7 @@ autoplot.BenchmarkResult = function(object, type = "boxplot", measure = NULL, th
     },
 
     "prc" = {
-      p = plot_precrec(object, curvetype = "PRC")
+      p = plot_precrec(object, curvetype = "PRC", ...)
       # fill confidence bounds
       p$layers[[1]]$mapping = aes(color = modname, fill = modname)
       p +

diff --git a/R/EnsembleFSResult.R b/R/EnsembleFSResult.R
@@ -77,7 +77,7 @@ autoplot.EnsembleFSResult = function(
   theme = theme_minimal(),
   ...
   ) {
-  assert_string(type)
+  assert_choice(type, choices = c("pareto", "performance", "n_features", "stability"), null.ok = FALSE)
   assert_choice(pareto_front, choices = c("stepwise", "estimated", "none"))
   result = object$result
   measure_id = object$measure

diff --git a/R/Filter.R b/R/Filter.R
@@ -31,7 +31,7 @@
 #'   autoplot(f, n = 5)
 #' }
 autoplot.Filter = function(object, type = "boxplot", n = Inf, theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("boxplot"), null.ok = FALSE)
 
   data = head(fortify(object), n)
 

diff --git a/R/LearnerClassif.R b/R/LearnerClassif.R
@@ -33,7 +33,7 @@
 #' }
 #' }
 autoplot.LearnerClassif = function(object, type = "prediction", task, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("prediction"), null.ok = FALSE)
 
   switch(type,
     "prediction" = {

diff --git a/R/LearnerClassifCVGlmnet.R b/R/LearnerClassifCVGlmnet.R
@@ -1,6 +1,8 @@
 #' @rdname autoplot.LearnerClassifGlmnet
 #' @export
 autoplot.LearnerClassifCVGlmnet = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
+  assert_choice(type, choices = c("prediction", "ggfortify"), null.ok = FALSE)
+
   switch(type,
     "prediction" = {
       NextMethod()

diff --git a/R/LearnerClassifGlmnet.R b/R/LearnerClassifGlmnet.R
@@ -42,6 +42,7 @@
 #' autoplot(learner, type = "ggfortify")
 #' }
 autoplot.LearnerClassifGlmnet = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
+  assert_choice(type, choices = c("prediction", "ggfortify"), null.ok = FALSE)
   assert_has_model(object)
 
   switch(type,

diff --git a/R/LearnerClassifRpart.R b/R/LearnerClassifRpart.R
@@ -38,6 +38,7 @@
 #'   autoplot(learner, type = "ggparty")
 #' }
 autoplot.LearnerClassifRpart = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
+  assert_choice(type, choices = c("prediction", "ggparty"), null.ok = FALSE)
   assert_has_model(object)
 
   switch(type,

diff --git a/R/LearnerClustHierarchical.R b/R/LearnerClustHierarchical.R
@@ -46,7 +46,7 @@
 #'   autoplot(learner, type = "scree")
 #' }
 autoplot.LearnerClustHierarchical = function(object, type = "dend", task = NULL, theme = theme_minimal(), theme_dendro = TRUE, ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("dend", "scree"), null.ok = FALSE)
 
   if (is.null(object$model)) {
     stopf("Learner '%s' must be trained first", object$id)

diff --git a/R/LearnerRegr.R b/R/LearnerRegr.R
@@ -33,7 +33,7 @@
 #' }
 #' }
 autoplot.LearnerRegr = function(object, type = "prediction", task, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("prediction"), null.ok = FALSE)
 
   switch(type,
     "prediction" = {

diff --git a/R/LearnerRegrCVGlmnet.R b/R/LearnerRegrCVGlmnet.R
@@ -1,6 +1,7 @@
 #' @rdname autoplot.LearnerClassifGlmnet
 #' @export
 autoplot.LearnerRegrCVGlmnet = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
+  assert_choice(type, choices = c("prediction", "ggfortify"), null.ok = FALSE)
   switch(type,
     "prediction" = {
       NextMethod()

diff --git a/R/LearnerRegrRpart.R b/R/LearnerRegrRpart.R
@@ -1,6 +1,7 @@
 #' @export
 #' @rdname autoplot.LearnerClassifRpart
 autoplot.LearnerRegrRpart = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
+  assert_choice(type, choices = c("prediction", "ggparty"), null.ok = FALSE)
   assert_has_model(object)
 
   switch(type,

diff --git a/R/LearnerSurvCoxPH.R b/R/LearnerSurvCoxPH.R
@@ -30,6 +30,7 @@
 #' }
 #' }
 autoplot.LearnerSurvCoxPH = function(object, type = "ggforest", ...) {
+  assert_choice(type, choices = c("ggforest"), null.ok = FALSE)
   assert_class(object, classes = "LearnerSurvCoxPH", null.ok = FALSE)
   assert_has_model(object)
 

diff --git a/R/OptimInstanceBatchSingleCrit.R b/R/OptimInstanceBatchSingleCrit.R
@@ -85,6 +85,8 @@
 #'   print(autoplot(instance, type = "incumbent"))
 #' }
 autoplot.OptimInstanceBatchSingleCrit = function(object, type = "marginal", cols_x = NULL, trafo = FALSE, learner = mlr3::lrn("regr.ranger"), grid_resolution = 100, batch = NULL, theme = theme_minimal(), ...) { # nolint
+  assert_choice(type, choices = c("marginal", "performance", "parameter", "parallel",
+                                  "points", "surface", "pairs", "incumbent"), null.ok = FALSE)
   assert_subset(cols_x, c(object$archive$cols_x, paste0("x_domain_", object$archive$cols_x)))
   assert_flag(trafo)
 

diff --git a/R/PredictionClassif.R b/R/PredictionClassif.R
@@ -41,7 +41,7 @@
 #' }
 #' }
 autoplot.PredictionClassif = function(object, type = "stacked", measure = NULL, theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("stacked", "roc", "prc", "threshold"), null.ok = FALSE)
 
   switch(type,
     "stacked" = {

diff --git a/R/PredictionClust.R b/R/PredictionClust.R
@@ -39,7 +39,7 @@
 #'   autoplot(object, task)
 #' }
 autoplot.PredictionClust = function(object, task, row_ids = NULL, type = "scatter", theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("scatter", "sil", "pca"), null.ok = FALSE)
 
   switch(type,
     "scatter" = {

diff --git a/R/PredictionRegr.R b/R/PredictionRegr.R
@@ -50,7 +50,7 @@
 #'  }
 #' }
 autoplot.PredictionRegr = function(object, type = "xy", binwidth = NULL, theme = theme_minimal(), quantile = 1.96, ...) {
-  checkmate::assert_string(type)
+  assert_choice(type, choices = c("xy", "histogram", "residual", "confidence"), null.ok = FALSE)
 
   switch(type,
     "xy" = {

diff --git a/R/ResampleResult.R b/R/ResampleResult.R
@@ -13,7 +13,7 @@
 #' * `"prc"`: Precision recall curve.
 #'    See `"roc"`.
 #' * `"prediction"`: Plots the learner prediction for a grid of points.
-#'    Needs models to be stored. Set `store_models = TRUE` for `[mlr3::resample]`.
+#'    Needs models to be stored. Set `store_models = TRUE` for [mlr3::resample()].
 #'    For classification, we support tasks with exactly two features and learners with `predict_type=` set to `"response"` or `"prob"`.
 #'    For regression, we support tasks with one or two features.
 #'    For tasks with one feature we can print confidence bounds if the predict type of the learner was set to `"se"`.
@@ -29,7 +29,8 @@
 #' @param binwidth (`integer(1)`)\cr
 #'  Width of the bins for the histogram.
 #' @template param_theme
-#' @param ... (ignored).
+#' @param ... arguments passed on to [precrec::autoplot()] for `type = "roc"` or `"prc"`.
+#' Useful to e.g. remove confidence bands with `show_cb = FALSE`.
 #'
 #' @return [ggplot2::ggplot()].
 #'
@@ -73,7 +74,7 @@
 #' }
 #' }
 autoplot.ResampleResult = function(object, type = "boxplot", measure = NULL, predict_sets = "test", binwidth = NULL, theme = theme_minimal(), ...) {
-  assert_string(type)
+  assert_choice(type, choices = c("boxplot", "histogram", "prediction", "roc", "prc"), null.ok = FALSE)
 
   task = object$task
   measure = mlr3::assert_measure(mlr3::as_measure(measure, task_type = task$task_type), task = task)
@@ -122,7 +123,7 @@ autoplot.ResampleResult = function(object, type = "boxplot", measure = NULL, pre
     },
 
     "prc" = {
-      p = plot_precrec(object, curvetype = "PRC")
+      p = plot_precrec(object, curvetype = "PRC", ...)
       # fill confidence bounds
       p$layers[[1]]$mapping = aes(color = modname, fill = modname)
       p +

diff --git a/R/TaskClassif.R b/R/TaskClassif.R
@@ -33,7 +33,7 @@
 #'   autoplot(task, type = "duo")
 #' }
 autoplot.TaskClassif = function(object, type = "target", theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("target", "duo", "pairs"), null.ok = FALSE)
 
   target = object$target_names
 

diff --git a/R/TaskClust.R b/R/TaskClust.R
@@ -27,7 +27,7 @@
 #'   autoplot(task)
 #' }
 autoplot.TaskClust = function(object, type = "pairs", theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("pairs"), null.ok = FALSE)
 
   switch(type,
     "pairs" = {

diff --git a/R/TaskRegr.R b/R/TaskRegr.R
@@ -30,7 +30,7 @@
 #'   autoplot(task, type = "pairs")
 #' }
 autoplot.TaskRegr = function(object, type = "target", theme = theme_minimal(), ...) { # nolint
-  assert_string(type)
+  assert_choice(type, choices = c("target", "pairs"), null.ok = FALSE)
 
   switch(type,
     "target" = {

diff --git a/man/autoplot.BenchmarkResult.Rd b/man/autoplot.BenchmarkResult.Rd
diff --git a/man/autoplot.ResampleResult.Rd b/man/autoplot.ResampleResult.Rd
diff --git a/tests/testthat/test_EnsembleFSResult.R b/tests/testthat/test_EnsembleFSResult.R
@@ -18,6 +18,10 @@ test_that("autoplot ResampleResult", {
   )
 
   efsr = mlr3fselect::EnsembleFSResult$new(result = result, features = paste0("V", 1:20), measure_id = "classif.ce")
+
+  # wrong type gives hint of types a user can input
+  expect_error(autoplot(efsr, type = "XYZ"), regexp = "Must be element of set")
+
   # pareto (stepwise)
   p = autoplot(efsr)
   expect_true(is.ggplot(p))