如何用mlr3中的分支解释GraphLearner的调优结果？

Question

最近我跟着一些教程学习如何在 mlr3 中使用 GraphLearner。但是我仍然对 GraphLearner with branch 的调优结果感到困惑。我设置了一个简单的例子，这是我的代码：

# Create a Graph with Branch
graph_branch <- 
  po("branch", c("nop", "pca", "scale"), id = "preprocess_branch") %>>%
  gunion(list(
    po("nop"),
    po("pca", id = "pca1"),
    po("scale") %>>% po("pca", id = "pca2")
    )) %>>%
  po("unbranch", id = "preprocess_unbranch") %>>%
  po("branch", c("classif.kknn", "classif.featureless"), id = "lrn_branch") %>>%
  gunion(list(
    lrn("classif.kknn", predict_type = "prob"),
    lrn("classif.featureless", predict_type = "prob")
    )) %>>%
  po("unbranch", id = "lrn_unbranch")

# Convert a graph to a learner
graph_branch_lrn <- as_learner(graph_branch)
graph_branch_lrn$graph$plot()

# Set the tuning grid
tune_grid <- ParamSet$new(
  list(
  ParamFct$new("preprocess_branch.selection", levels = c("nop", "pca", "scale")),
  ParamInt$new("pca1.rank.", lower = 1, upper = 10),
  ParamInt$new("pca2.rank.", lower = 1, upper = 10),
  ParamFct$new("lrn_branch.selection", levels = c("classif.kknn", "classif.featureless")),
  ParamInt$new("classif.kknn.k", lower = 1, upper = 10)
  ))

# Set the instance
instance_rs <- TuningInstanceSingleCrit$new(
  task = task_train,
  learner = graph_branch_lrn,
  resampling = rsmp("cv", folds = 5),
  measure = msr("classif.auc"),
  search_space = tune_grid,
  terminator = trm("evals", n_evals = 20)
  )

# Random search tuning
tuner_rs <- tnr("random_search")
plan(multisession, workers = 5)
set.seed(100)
tuner_rs$optimize(instance_rs)
plan(sequential)

最好的调优结果是：

# Check the result
instance_rs$result_learner_param_vals

$preprocess_branch.selection
[1] "nop"

$scale.robust
[1] FALSE

$lrn_branch.selection
[1] "classif.kknn"

$classif.featureless.method
[1] "mode"

$pca1.rank.
[1] 9

$pca2.rank.
[1] 9

$classif.kknn.k
[1] 9

我想知道如果选择“nop”分支，为什么“pca1.rank”的调优结果。和“pca2.rank”。出现？我曾经考虑过，GraphLearner with branch 的调优会根据分支选择最好的结果，比如如果选择了“nop”分支，其他分支中的参数就不会考虑并出现在过程中。我是在解释 GraphLearner 调优的机制时出了什么问题，还是在代码中出了什么问题？

Answer 1

你没有做错任何事；输出只显示 all 超参数的最佳值。并非所有这些都是相关的——在这里，您可以简单地忽略 scale、pca1 和 pca2 的值。