r - mlr3 中的嵌套分支(和依赖项)

Question

我正在尝试使用 information_gain 和 mrmr 特征过滤，但也尝试使用 information_gain 和 mrmr 特征过滤的组合(两者的结合)。我试过在下面创建一个reprex。

library("mlr3verse")
task <- tsk('sonar')


filters = list("nop" = po("nop"),
               "information_gain" = po("filter", flt("information_gain")),
               "mrmr" = po("filter", flt("mrmr")),
               "ig_mrmr" = po("branch", c("ig2", "mrmr2"), id = "ig_mrmr") %>>%
                 gunion(list("ig2" = po("filter", flt("information_gain")),
                             "mrmr2" = po("filter", flt("mrmr")))) %>>%
                 po("featureunion", id = "union_igmrmr"))

pipe =
  po("branch", names(filters), id = "branch1") %>>%
  gunion(unname(filters)) %>>%
  po("unbranch", names(filters), id = "unbranch1") %>>%
  po(lrn('classif.rpart'))

pipe$plot()

pipe plot
到目前为止看起来不错，在这里您可以看到我正在尝试结合 ig 和 mrmr 选定的功能。
接下来我设置参数，我认为是正确的:

ps <- ParamSet$new(list(
  ParamDbl$new("classif.rpart.cp", lower = 0, upper = 0.05),
  ParamInt$new("information_gain.filter.nfeat", lower = 20L, upper = 60L),
  ParamFct$new("information_gain.type", levels = c("infogain", "symuncert")),
  ParamInt$new("ig2.filter.nfeat", lower = 20L, upper = 60L),
  ParamFct$new("ig2.type", levels = c("infogain", "symuncert")),
  ParamInt$new("mrmr.filter.nfeat", lower = 20L, upper = 60L),
  ParamInt$new("mrmr2.filter.nfeat", lower = 20L, upper = 60L),
  ParamFct$new("branch1.selection", levels = names(filters)),
  ParamFct$new("ig_mrmr.selection", levels = c("ig2", "mrmr2"))
))

依赖是我挣扎的地方。我可以在外部分支或内部分支上设置“嵌套”参数，但我不确定如何在两者上触发它们。在下面的示例中，它们设置在外部分支上。

ps$add_dep("information_gain.filter.nfeat", "branch1.selection", CondEqual$new("information_gain"))
ps$add_dep("information_gain.type", "branch1.selection", CondEqual$new("information_gain"))
ps$add_dep("mrmr.filter.nfeat", "branch1.selection", CondEqual$new("mrmr"))
ps$add_dep("ig2.filter.nfeat", "branch1.selection", CondEqual$new("ig_mrmr"))
ps$add_dep("ig2.type", "branch1.selection", CondEqual$new("ig_mrmr"))
ps$add_dep("mrmr2.filter.nfeat", "branch1.selection", CondEqual$new("ig_mrmr"))

ps

glrn <- GraphLearner$new(pipe) 

glrn$predict_type <- "prob"

cv5 <- rsmp("cv", folds = 5)

task$col_roles$stratum <- task$target_names

instance <- TuningInstanceSingleCrit$new(
  task = task,
  learner = glrn,
  resampling = cv5,
  measure = msr("classif.auc"),
  search_space = ps,
  terminator = trm("evals", n_evals = 5)
)

tuner <- tnr("random_search")
tuner$optimize(instance)

请注意，在尝试优化调谐器之前，我不会遇到错误。
错误信息:

Error in self$assert(xs) : 
  Assertion on 'xs' failed: Parameter 'ig2.filter.nfeat' not available. Did you mean 'branch1.selection' / 'information_gain.filter.nfeat' / 'information_gain.filter.frac'?.

Answer 1

从您的描述来看，您似乎不打算为 c("ig2", "mrmr2") 使用分支。 :

po("branch", c("ig2", "mrmr2"), id = "ig_mrmr") %>>%
                 gunion(list("ig2" = po("filter", flt("information_gain")),
                             "mrmr2" = po("filter", flt("mrmr")))) %>>%
                 po("featureunion", id = "union_igmrmr")

因为您打算将这两者的输出结合起来。换句话说，您希望它们都应用在同一个重采样实例中。

library("mlr3verse")
task <- tsk('sonar')
filters = list("nop" = po("nop"),
               "information_gain" = po("filter", flt("information_gain")),
               "mrmr" = po("filter", flt("mrmr")),
               "ig_mrmr" = po("copy", 2) %>>%
                 gunion(list("ig2" = po("filter", flt("information_gain")),
                                       "mrmr2" = po("filter", flt("mrmr")))) %>>%
                 po("featureunion", id = "union_igmrmr"))

pipe = po("branch", names(filters), id = "branch1") %>>%
  gunion(unname(filters)) %>>%
  po("unbranch", names(filters), id = "unbranch1") %>>%
  po(lrn('classif.rpart'))

pipe$plot()

查看可以调整的参数的最简单方法是:

pipe$param_set

从这里你会看到你指定的一些参数没有全名。例如:

15:   ig2.information_gain.filter.nfeat ParamInt     0   Inf                                   <NoDefault[3]>      
16:    ig2.information_gain.filter.frac ParamDbl     0     1                                   <NoDefault[3]>      
17:  ig2.information_gain.filter.cutoff ParamDbl  -Inf   Inf                                   <NoDefault[3]>      
18:           ig2.information_gain.type ParamFct    NA    NA      infogain,gainratio,symuncert       infogain      
19:          ig2.information_gain.equal ParamLgl    NA    NA                        TRUE,FALSE          FALSE      
20:   ig2.information_gain.discIntegers ParamLgl    NA    NA                        TRUE,FALSE           TRUE      
21:        ig2.information_gain.threads ParamInt     0   Inf                                                1      
22: ig2.information_gain.affect_columns ParamUty    NA    NA                                    <Selector[1]>      
23:             mrmr2.mrmr.filter.nfeat ParamInt     0   Inf                                   <NoDefault[3]>      
24:              mrmr2.mrmr.filter.frac ParamDbl     0     1                                   <NoDefault[3]>      
25:            mrmr2.mrmr.filter.cutoff ParamDbl  -Inf   Inf                                   <NoDefault[3]>      
26:                  mrmr2.mrmr.threads ParamInt     0   Inf                                                0      
27:           mrmr2.mrmr.affect_columns ParamUty    NA    NA                                    <Selector[1]>      

让我们为参数指定正确的名称:

ps = ParamSet$new(list(
  ParamDbl$new("classif.rpart.cp", lower = 0, upper = 0.05),
  ParamInt$new("information_gain.filter.nfeat", lower = 20L, upper = 60L),
  ParamFct$new("information_gain.type", levels = c("infogain", "symuncert")),
  ParamInt$new("ig2.information_gain.filter.nfeat", lower = 20L, upper = 60L),
  ParamFct$new("ig2.information_gain.type", levels = c("infogain", "symuncert")),
  ParamInt$new("mrmr.filter.nfeat", lower = 20L, upper = 60L),
  ParamInt$new("mrmr2.mrmr.filter.nfeat", lower = 20L, upper = 60L),
  ParamFct$new("branch1.selection", levels = names(filters))
))

ps$add_dep("information_gain.filter.nfeat", "branch1.selection", CondEqual$new("information_gain"))
ps$add_dep("information_gain.type", "branch1.selection", CondEqual$new("information_gain"))
ps$add_dep("mrmr.filter.nfeat", "branch1.selection", CondEqual$new("mrmr"))
ps$add_dep("ig2.information_gain.filter.nfeat", "branch1.selection", CondEqual$new("ig_mrmr"))
ps$add_dep("ig2.information_gain.type", "branch1.selection", CondEqual$new("ig_mrmr"))
ps$add_dep("mrmr2.mrmr.filter.nfeat", "branch1.selection", CondEqual$new("ig_mrmr"))

现在一切运行都没有问题:

glrn <- GraphLearner$new(pipe) 

glrn$predict_type <- "prob"

cv5 <- rsmp("cv", folds = 5)

task$col_roles$stratum <- task$target_names

instance <- TuningInstanceSingleCrit$new(
  task = task,
  learner = glrn,
  resampling = cv5,
  measure = msr("classif.auc"),
  search_space = ps,
  terminator = trm("evals", n_evals = 5)
)

tuner <- tnr("random_search")
tuner$optimize(instance)

instance$result
   classif.rpart.cp information_gain.filter.nfeat information_gain.type ig2.information_gain.filter.nfeat ig2.information_gain.type mrmr.filter.nfeat mrmr2.mrmr.filter.nfeat branch1.selection
1:       0.01956043                            NA                  <NA>                                44                 symuncert                NA                      34           ig_mrmr
   learner_param_vals  x_domain classif.auc
1:          <list[6]> <list[5]>   0.7187196

这个画廊帖子将很有用:
https://mlr3gallery.mlr-org.com/posts/2020-04-23-pipelines-selectors-branches/
和其他人一样
https://mlr3gallery.mlr-org.com/
如果您觉得 mlr3 的某些方面无法理解并且您找不到合适的画廊帖子/书籍示例，您应该请求它。
图书链接: https://mlr3book.mlr-org.com/