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我正在尝试使用 Tidymodels 预测 R 中的房地产价格。我正在关注本教程。一切都很顺利，直到我尝试对我的测试数据进行预测。

请参阅下面的代码示例和最后的错误。

我查看了两个类似的问题（此处和此处），但似乎我已经定义了可变角色并为我的工作流程提供了一个未准备好的配方。

    # libraries ---------------------------------------------------------------
    library(tidymodels)
    #> ── Attaching packages ────────────────────────────────────── tidymodels 0.1.2 ──
    #> ✓ broom     0.7.3      ✓ recipes   0.1.15
    #> ✓ dials     0.0.9      ✓ rsample   0.0.8 
    #> ✓ dplyr     1.0.3      ✓ tibble    3.0.5 
    #> ✓ ggplot2   3.3.3      ✓ tidyr     1.1.2 
    #> ✓ infer     0.5.4      ✓ tune      0.1.2 
    #> ✓ modeldata 0.1.0      ✓ workflows 0.2.1 
    #> ✓ parsnip   0.1.5      ✓ yardstick 0.0.7 
    #> ✓ purrr     0.3.4
    #> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
    #> x purrr::discard() masks scales::discard()
    #> x dplyr::filter()  masks stats::filter()
    #> x dplyr::lag()     masks stats::lag()
    #> x recipes::step()  masks stats::step()
    library(data.table)
    
    library(purrr)
    
    
    # data --------------------------------------------------------------------
    # 're' means real estate
    # I'm using data.table in general. Using tribble below for cleaner data definition.
    real_estate_data <- tibble::tribble(
        ~re_id, ~price_per_sqm_huf_mil, ~district, ~num_room,
        "30876343",      0.534722222222222,        1,         3,
        "31914489",      0.476119402985075,        1,         1,
        "30972289",      0.507352941176471,        1,         2,
        "31739730",      0.472972972972973,        1,         3,
        "31783137",                0.49875,        2,         3,
        "31809435",      0.439705882352941,        2,         2,
        "31943408",      0.469117647058824,        2,         3,
        "31944348",       0.56231884057971,        2,         1,
        "31961146",      0.472972972972973,        3,         3,
        "24314388",      0.649550561797753,        3,         2,
        "29840270",      0.719178082191781,        3,         3,
        "29840429",      0.719178082191781,        3,         3,
        "30873484",      0.822857142857143,        4,         3,
        "30969673",      0.533802816901408,        4,         3,
        "31333120",      0.741511627906977,        4,         3,
        "31788730",      0.527142857142857,        4,         2,
        "31948441",      0.734848484848485,        5,         2,
        "31962350",                    0.8,        5,         3,
        "31962779",      0.670454545454545,        5,         3,
        "31979128",      0.689054054054054,        5,         1
    )
    
    real_estate_data <- as.data.table(real_estate_data) %>% .[, district := factor(district)]
    
    # train/test split --------------------------------------------------------
    set.seed(123)
    re_split <- initial_split(real_estate_data)
    re_train <- training(re_split)
    re_test  <- testing(re_split)
    
    # workflow (w/ recipe) ----------------------------------------------------
    re_rec <- recipe(re_train,
                     formula = price_per_sqm_huf_mil ~ .) %>%
        update_role(re_id, new_role = "ID") %>%
        step_center(all_numeric(), - district) %>%
        step_scale(all_predictors(), all_numeric(), - district) %>%
        step_dummy(district) %>%
        step_zv(all_predictors())
    
    summary(re_rec)
    #> # A tibble: 4 x 4
    #>   variable              type    role      source  
    #>   <chr>                 <chr>   <chr>     <chr>   
    #> 1 re_id                 nominal ID        original
    #> 2 district              nominal predictor original
    #> 3 num_room              numeric predictor original
    #> 4 price_per_sqm_huf_mil numeric outcome   original
    
    lr_model <-
        linear_reg() %>%
        set_engine("lm")
    
    re_wflow <-
        workflow() %>%
        add_model(lr_model) %>%
        add_recipe(re_rec)
    
    # model training and prediction -------------------------------------------
    re_fit <-
        re_wflow %>%
        fit(data = re_train)
    
    re_pred <- predict(re_fit, re_test)
    #> Error: Can't subset columns that don't exist.
    #> x Column `price_per_sqm_huf_mil` doesn't exist.

由reprex 包于 2021-01-25 创建(v0.3.0)

非常感谢！

4

1 回答 1

5

这里的问题是您曾经step_center()转换结果( price_per_sqm_huf_mil) 并且在预测时，没有可用的结果。您可以改为指定要居中，all_predictors() & all_numeric()如下所示：

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 0.1.2 ──
#> ✓ broom     0.7.3      ✓ recipes   0.1.15
#> ✓ dials     0.0.9      ✓ rsample   0.0.8 
#> ✓ dplyr     1.0.3      ✓ tibble    3.0.5 
#> ✓ ggplot2   3.3.3      ✓ tidyr     1.1.2 
#> ✓ infer     0.5.4      ✓ tune      0.1.2 
#> ✓ modeldata 0.1.0      ✓ workflows 0.2.1 
#> ✓ parsnip   0.1.5      ✓ yardstick 0.0.7 
#> ✓ purrr     0.3.4
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter()  masks stats::filter()
#> x dplyr::lag()     masks stats::lag()
#> x recipes::step()  masks stats::step()
library(dplyr)

real_estate_data <- tibble::tribble(
  ~re_id, ~price_per_sqm_huf_mil, ~district, ~num_room,
  "30876343",      0.534722222222222,        1,         3,
  "31914489",      0.476119402985075,        1,         1,
  "30972289",      0.507352941176471,        1,         2,
  "31739730",      0.472972972972973,        1,         3,
  "31783137",                0.49875,        2,         3,
  "31809435",      0.439705882352941,        2,         2,
  "31943408",      0.469117647058824,        2,         3,
  "31944348",       0.56231884057971,        2,         1,
  "31961146",      0.472972972972973,        3,         3,
  "24314388",      0.649550561797753,        3,         2,
  "29840270",      0.719178082191781,        3,         3,
  "29840429",      0.719178082191781,        3,         3,
  "30873484",      0.822857142857143,        4,         3,
  "30969673",      0.533802816901408,        4,         3,
  "31333120",      0.741511627906977,        4,         3,
  "31788730",      0.527142857142857,        4,         2,
  "31948441",      0.734848484848485,        5,         2,
  "31962350",                    0.8,        5,         3,
  "31962779",      0.670454545454545,        5,         3,
  "31979128",      0.689054054054054,        5,         1
) %>%
  mutate(district = factor(district))


set.seed(123)
re_split <- initial_split(real_estate_data)
re_train <- training(re_split)
re_test  <- testing(re_split)

re_rec <- recipe(re_train,
                 formula = price_per_sqm_huf_mil ~ .) %>%
  update_role(re_id, new_role = "ID") %>%
  step_center(all_predictors() & all_numeric()) %>%
  step_scale(all_predictors() & all_numeric()) %>%
  step_dummy(district) %>%
  step_zv(all_predictors())

summary(re_rec)
#> # A tibble: 4 x 4
#>   variable              type    role      source  
#>   <chr>                 <chr>   <chr>     <chr>   
#> 1 re_id                 nominal ID        original
#> 2 district              nominal predictor original
#> 3 num_room              numeric predictor original
#> 4 price_per_sqm_huf_mil numeric outcome   original

lr_model <-
  linear_reg() %>%
  set_engine("lm")

re_wflow <-
  workflow() %>%
  add_model(lr_model) %>%
  add_recipe(re_rec)

re_fit <-
  re_wflow %>%
  fit(data = re_train)

predict(re_fit, new_data = re_test)
#> # A tibble: 5 x 1
#>   .pred
#>   <dbl>
#> 1 0.486
#> 2 0.611
#> 3 0.688
#> 4 0.688
#> 5 0.768

由reprex 包于 2021-01-25 创建(v0.3.0)

这让比你更多的人绊倒了，所以我们正在努力添加一组新的选择器，这些选择器很快就会被合并。如果你真的想尝试改变结果，另一个要考虑的选择是考虑使用skip = TRUE.

于 2021-01-25T20:47:32.483 回答