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我recipe()在包中使用函数tidymodels来估算缺失值和修复不平衡的数据。

这是我的数据；

mer_df <- mer2 %>%
  filter(!is.na(laststagestatus2)) %>% 
  select(Id, Age_Range__c, Gender__c, numberoflead, leadduration, firsttouch, lasttouch, laststagestatus2)%>%
  mutate_if(is.character, factor) %>%
  mutate_if(is.logical, as.integer)


# A tibble: 197,836 x 8
   Id    Age_Range__c Gender__c numberoflead leadduration firsttouch lasttouch
   <fct> <fct>        <fct>            <int>        <dbl> <fct>      <fct>    
 1 0010~ NA           NA                   2     5.99     Dealer IB~ Walk in  
 2 0010~ NA           NA                   1     0        Online Se~ Online S~
 3 0010~ NA           NA                   1     0        Walk in    Walk in  
 4 0010~ NA           NA                   1     0        Online Se~ Online S~
 5 0010~ NA           NA                   2     0.0128   Dealer IB~ Dealer I~
 6 0010~ NA           NA                   1     0        OB Call    OB Call  
 7 0010~ NA           NA                   1     0        Dealer IB~ Dealer I~
 8 0010~ NA           NA                   4    73.9      Dealer IB~ Walk in  
 9 0010~ NA           Male                24     0.000208 OB Call    OB Call  
10 0010~ NA           NA                  18     0.000150 OB Call    OB Call  
# ... with 197,826 more rows, and 1 more variable: laststagestatus2 <fct>

这是我的代码；

mer_rec <- recipe(laststagestatus2 ~ ., data = mer_train)%>%
  step_medianimpute(numberoflead,leadduration)%>%
  step_knnimpute(Gender__c,Age_Range__c,fisrsttouch,lasttouch) %>% 
  step_other(Id,firsttouch) %>% 
  step_other(Id,lasttouch) %>% 
  step_dummy(all_nominal(), -laststagestatus2) %>% 
  step_smote(laststagestatus2)
mer_rec
mer_rec %>% prep()

直到这里都可以正常工作；

Data Recipe

Inputs:

      role #variables
   outcome          1
 predictor          7

Training data contained 148377 data points and 147597 incomplete rows. 

    Operations:
    
    Median Imputation for 2 items [trained]
    K-nearest neighbor imputation for Id, ... [trained]
    Collapsing factor levels for Id, firsttouch [trained]
    Collapsing factor levels for Id, lasttouch [trained]
    Dummy variables from Id, ... [trained]
    SMOTE based on laststagestatus2 [trained]

但是当我运行bake()给出错误的函数时说；

mer_rec %>% prep() %>% bake(new_data=NULL) %>% count(laststagestatus2)
Error: Please pass a data set to `new_data`.

任何人都可以帮助我了解我在这里缺少的东西吗？

4

1 回答 1

2

配方的开发版本中有一个修复程序可以启动和工作。您可以通过以下方式安装：

devtools::install_github("tidymodels/recipes")

然后你可以bake()用new_data = NULL得到转换后的训练数据。

library(tidymodels)
data(ames)
ames <- mutate(ames, Sale_Price = log10(Sale_Price))

set.seed(123)
ames_split <- initial_split(ames, prob = 0.80, strata = Sale_Price)
ames_train <- training(ames_split)
ames_test  <-  testing(ames_split)

ames_rec <- 
  recipe(Sale_Price ~ Neighborhood + Gr_Liv_Area + Year_Built + Bldg_Type + 
           Latitude + Longitude, data = ames_train) %>%
  step_log(Gr_Liv_Area, base = 10) %>% 
  step_other(Neighborhood, threshold = 0.01) %>% 
  step_dummy(all_nominal()) %>% 
  step_interact( ~ Gr_Liv_Area:starts_with("Bldg_Type_") ) %>% 
  step_ns(Latitude, Longitude, deg_free = 20)

ames_rec %>% prep() %>% bake(new_data = NULL)
#> # A tibble: 2,199 x 71
#>    Gr_Liv_Area Year_Built Sale_Price Neighborhood_Co… Neighborhood_Ol…
#>          <dbl>      <int>      <dbl>            <dbl>            <dbl>
#>  1        3.22       1960       5.33                0                0
#>  2        2.95       1961       5.02                0                0
#>  3        3.12       1958       5.24                0                0
#>  4        3.21       1997       5.28                0                0
#>  5        3.21       1998       5.29                0                0
#>  6        3.13       2001       5.33                0                0
#>  7        3.11       1992       5.28                0                0
#>  8        3.21       1995       5.37                0                0
#>  9        3.22       1993       5.25                0                0
#> 10        3.17       1998       5.26                0                0
#> # … with 2,189 more rows, and 66 more variables: Neighborhood_Edwards <dbl>,
#> #   Neighborhood_Somerset <dbl>, Neighborhood_Northridge_Heights <dbl>,
#> #   Neighborhood_Gilbert <dbl>, Neighborhood_Sawyer <dbl>,
#> #   Neighborhood_Northwest_Ames <dbl>, Neighborhood_Sawyer_West <dbl>,
#> #   Neighborhood_Mitchell <dbl>, Neighborhood_Brookside <dbl>,
#> #   Neighborhood_Crawford <dbl>, Neighborhood_Iowa_DOT_and_Rail_Road <dbl>,
#> #   Neighborhood_Timberland <dbl>, Neighborhood_Northridge <dbl>,
#> #   Neighborhood_Stone_Brook <dbl>,
#> #   Neighborhood_South_and_West_of_Iowa_State_University <dbl>,
#> #   Neighborhood_Clear_Creek <dbl>, Neighborhood_Meadow_Village <dbl>,
#> #   Neighborhood_other <dbl>, Bldg_Type_TwoFmCon <dbl>, Bldg_Type_Duplex <dbl>,
#> #   Bldg_Type_Twnhs <dbl>, Bldg_Type_TwnhsE <dbl>,
#> #   Gr_Liv_Area_x_Bldg_Type_TwoFmCon <dbl>,
#> #   Gr_Liv_Area_x_Bldg_Type_Duplex <dbl>, Gr_Liv_Area_x_Bldg_Type_Twnhs <dbl>,
#> #   Gr_Liv_Area_x_Bldg_Type_TwnhsE <dbl>, Latitude_ns_01 <dbl>,
#> #   Latitude_ns_02 <dbl>, Latitude_ns_03 <dbl>, Latitude_ns_04 <dbl>,
#> #   Latitude_ns_05 <dbl>, Latitude_ns_06 <dbl>, Latitude_ns_07 <dbl>,
#> #   Latitude_ns_08 <dbl>, Latitude_ns_09 <dbl>, Latitude_ns_10 <dbl>,
#> #   Latitude_ns_11 <dbl>, Latitude_ns_12 <dbl>, Latitude_ns_13 <dbl>,
#> #   Latitude_ns_14 <dbl>, Latitude_ns_15 <dbl>, Latitude_ns_16 <dbl>,
#> #   Latitude_ns_17 <dbl>, Latitude_ns_18 <dbl>, Latitude_ns_19 <dbl>,
#> #   Latitude_ns_20 <dbl>, Longitude_ns_01 <dbl>, Longitude_ns_02 <dbl>,
#> #   Longitude_ns_03 <dbl>, Longitude_ns_04 <dbl>, Longitude_ns_05 <dbl>,
#> #   Longitude_ns_06 <dbl>, Longitude_ns_07 <dbl>, Longitude_ns_08 <dbl>,
#> #   Longitude_ns_09 <dbl>, Longitude_ns_10 <dbl>, Longitude_ns_11 <dbl>,
#> #   Longitude_ns_12 <dbl>, Longitude_ns_13 <dbl>, Longitude_ns_14 <dbl>,
#> #   Longitude_ns_15 <dbl>, Longitude_ns_16 <dbl>, Longitude_ns_17 <dbl>,
#> #   Longitude_ns_18 <dbl>, Longitude_ns_19 <dbl>, Longitude_ns_20 <dbl>

由reprex 包（v0.3.0.9001）于 2020 年 10 月 12 日创建

如果你无法从 GitHub 安装包，你可以使用juice()做同样的事情。

于 2020-10-12T17:37:49.230 回答