r - 有没有办法在 R(或 ml3)的食谱包中对行(尤其是虚拟变量)进行分组

Question

# Packages
library(dplyr)
library(recipes)

# toy dataset, with A being multicolored
df <- tibble(name = c("A", "A", "A", "B", "C"), color = c("green", "yellow", "purple", "green", "blue"))


    #> # A tibble: 5 x 2
    #>   name  color 
    #>   <chr> <chr> 
    #> 1 A     green 
    #> 2 A     yellow
    #> 3 A     purple
    #> 4 B     green 
    #> 5 C     blue

食谱步骤效果很好

dummified_df <- recipe(. ~ ., data = df) %>%
        step_dummy(color, one_hot = TRUE) %>%
        prep(training = df) %>%
        juice()


    #> # A tibble: 5 x 5
    #>   name  color_blue color_green color_purple color_yellow
    #>   <fct>      <dbl>       <dbl>        <dbl>        <dbl>
    #> 1 A              0           1            0            0
    #> 2 A              0           0            0            1
    #> 3 A              0           0            1            0
    #> 4 B              0           1            0            0
    #> 5 C              1           0            0            0

但我真正想要获得的结果是下面的结果，每行一个观察结果，因为彩色项目不再需要多行。

summarized_dummified_df <- dummified_df %>% 
     group_by(name) %>% 
     summarise_all(~ifelse(max(.) > 0, 1, 0)) %>% 
     ungroup()


    #> # A tibble: 3 x 5
    #>   name  color_blue color_green color_purple color_yellow
    #>   <fct>      <dbl>       <dbl>        <dbl>        <dbl>
    #> 1 A              0           1            1            1
    #> 2 B              0           1            0            0
    #> 3 C              1           0            0            0

显然，我可以这样做。但是为了将我的配方步骤完全集成到 tidymodels 生态系统中，例如与工作流，如果我可以直接对不需要重复的行进行分组会更好，这要归功于虚拟变量在食谱里面。

是否有任何tidymodels-sanctioned方法来获得这个结果？

---

我也尝试用 mlr3 来做这件事，但没有成功，因为我找不到任何合适的 PipeOp 来聚合行。

library("mlr3")
library("mlr3pipelines")


task = TaskClassif$new("task",
                       data.table::data.table(
                           name = c("A", "A", "A", "B", "C"),
                           color = as.factor(c("green", "yellow", "purple", "green", "blue")),
                           price = as.factor(c("low", "low", "low", "high", "low"))),
                           "price"
                       )
                       
poe = po("encode")

poe$train(list(task))[[1]]$data()

#>    price name color.blue color.green color.purple color.yellow
#> 1:   low    A          0           1            0            0
#> 2:   low    A          0           0            0            1
#> 3:   low    A          0           0            1            0
#> 4:  high    B          0           1            0            0
#> 5:   low    C          1           0            0            0

---

我正在研究 custom step_ 的创建过程函数或 custom PipeOp但我仍然觉得我遗漏了什么，因为我觉得我的数据类型对我来说并不罕见。

Answer 1

在我所见的任何地方，虚拟变量或指示变量在概念上都被映射为一对一，而不是一对多，我认为这就是您遇到此问题的原因。不过，像你一样，我也想在现实世界中的某个时候将它们一对多映射。我通常在开始模型预处理工作流之前在数据整理步骤中执行此操作，如下所示:

library(tidyverse)

# toy dataset, with A being multicolored
df <- tibble(name = c("A", "A", "A", "B", "C"), color = c("green", "yellow", "purple", "green", "blue"))

df %>%
  mutate(value = 1) %>%
  pivot_wider(names_from = "color", names_prefix = "color_", values_from = "value", values_fill = 0)
#> # A tibble: 3 x 5
#>   name  color_green color_yellow color_purple color_blue
#>   <chr>       <dbl>        <dbl>        <dbl>      <dbl>
#> 1 A               1            1            1          0
#> 2 B               1            0            0          0
#> 3 C               0            0            0          1

^{由 reprex package 创建于 2020-08-18 (v0.3.0.9001)}