Tidymodels：在日期列中估算缺失值？

Question

此问题与Tidymodels: What is the correct way to impute missing values in a Date column? 重复 问题结束后，我提供了reprex 并再次提出问题。

我有点为日期列中的缺失值而苦恼。 在我的预处理管道 (recipe-object) 中，我使用了step_impute_knn 函数来填充我所有日期列中的缺失值。不幸的是，我收到以下错误：

分配的数据 pred_vals 必须与现有数据兼容。？列 avg_begin_first_contract .x 发生错误无法将双精度转换为日期

这是一个reprex，用于我在多个列中估算值的版本，包括Date 列。如果我只将值估算到Date 列，这对我来说并不重要。结果是一样的。下面有一个reprex，没有通过错误，因为没有使用Datecolumn。

以前有人遇到过这个问题吗？

library(tidyverse)
library(tidymodels)

iris <- iris %>%
  mutate(Plucked = sample(seq(as.Date("1999/01/01"), as.Date("2000/01/01"),
    by = "day"
  ), size = 150))

iris[45, 2] <- as.numeric(NA)
iris[37, 3] <- as.numeric(NA)
iris[78, 4] <- as.numeric(NA)
iris[9, 5] <- as.numeric(NA)
iris[15, 6] <- as.factor(NA)

set.seed(456)

iris_split <- iris %>%
  initial_split(strata = Sepal.Length)


iris_training <- training(iris_split)
iris_testing <- testing(iris_split)

iris_rf_model <- rand_forest(
  mtry = 10,
  min_n = 10,
  trees = 500
) %>%
  set_engine("ranger") %>%
  set_mode("regression")


base_rec <- recipe(Sepal.Length ~ .,
  data = iris_training
) %>%
  step_impute_knn(Sepal.Width, Petal.Length, Petal.Width, Species, Plucked) %>%
  step_date(Plucked) %>%
  step_dummy(Species)

iris_workflow <- workflow() %>%
  add_model(iris_rf_model) %>%
  add_recipe(base_rec)

iris_rf_wkfl_fit <- iris_workflow %>%
  last_fit(iris_split)
#> x train/test split: preprocessor 1/1: Error: Assigned data `pred_vals` must be compatible wi...
#> Warning: All models failed. See the `.notes` column.
Created on 2021-06-15 by the reprex package (v2.0.0)

这是reprex，它不会通过错误：

library(tidyverse)
library(tidymodels)

iris[45, 2] <- as.numeric(NA)
iris[37 ,3] <- as.numeric(NA)
iris[78, 4] <- as.numeric(NA)
iris[9, 5] <- as.numeric(NA)

set.seed(123)

iris_split <- iris %>% 
  initial_split(strata = Sepal.Length)

iris_training <- training(iris_split)
iris_testing <- testing(iris_split)

iris_rf_model <- rand_forest(
  mtry = 5,
  min_n = 5,
  trees = 500) %>%
  set_engine("ranger") %>%
  set_mode("regression")


base_rec <- recipe(Sepal.Length ~ .,
                   data = iris_training) %>% 
  step_impute_knn(Sepal.Width, Petal.Length, Petal.Width, Species) %>%
  step_dummy(Species)

iris_workflow <- workflow() %>% 
  add_model(iris_rf_model) %>% 
  add_recipe(base_rec)

iris_rf_wkfl_fit <- iris_workflow %>%
  last_fit(split = iris_split)
Created on 2021-06-15 by the reprex package (v2.0.0)

提前致谢！ M.

Answer 1

我怀疑step_impute_knn 不适用于日期格式。您可能必须先将其转换为因子。你可以试试下面的代码吗？

iris_n <- iris %>%
  mutate(Plucked = sample(seq(as.Date("1999/01/01"), as.Date("2000/01/01"),
    by = "day"
  ), size = 150))  %>% 
  mutate(Plucked = as.factor(Plucked)) #convert date into factor

iris_n[45, 2] <- NA
iris_n[37, 3] <- NA
iris_n[78, 4] <- NA
iris_n[9, 5] <- NA
iris_n[15, 6] <- NA

set.seed(456)

iris_split <- iris_n %>%
  initial_split(strata = Sepal.Length)


iris_training <- training(iris_split)
iris_testing <- testing(iris_split)

iris_rf_model <- rand_forest(
  mtry = 10,
  min_n = 10,
  trees = 500
) %>%
  set_engine("ranger") %>%
  set_mode("regression")


base_rec <- recipe(Sepal.Length ~ .,
  data = iris_training
) %>%
  step_impute_knn(Sepal.Width, Petal.Length, Petal.Width, Species, Plucked) %>%
  #step_date(Plucked) %>% #might not need this step anymore
  step_dummy(Species)

iris_workflow <- workflow() %>%
  add_model(iris_rf_model) %>%
  add_recipe(base_rec)

iris_rf_wkfl_fit <- iris_workflow %>%
  last_fit(iris_split)