将变量列表传递给 tidymodels 中的配方会导致模型错误
passing a list of variables to recipe in tidymodels causes model error
我有一个训练模型的简单方法。我的分类变量随时间变化，有时我希望将数字视为分类变量（邮政编码），因此我在包含它们的配方之前定义了一个列表。 （只是为了论证，列表要长得多）
配方工作正常，然后训练我的模型（3 次）但出现错误。
model_tuned$.notes
[[1]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[2]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[3]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
是否有任何正确的方法将变量列表传递给配方而不会使模型崩溃？
REPREX
library(recipes)
library(magrittr)
library(tidyverse)
library(xgboost)
library(tidymodels)
mtcars1 <- mtcars
mtcars1 %<>% dplyr::mutate(new1 = sample.int(200, 32, replace = TRUE),
new2 = sample.int(100, 32, replace = TRUE),
new3 = sample.int(50, 32, replace = TRUE))
my_categorical <- c("new1", "new2", "new3")
mtcars_split <- initial_split(mtcars1, strata = drat)
train <- training(mtcars_split)
test <- testing(mtcars_split)
recipe <-
recipes::recipe(drat ~ ., data = train) %>%
recipes::step_mutate_at(all_of(my_categorical), fn = ~as.character(.)) %>%
recipes::step_string2factor(all_of(my_categorical)) %>%
prep()
cv_folds <-
vfold_cv(train,
v = 3,
strata = drat)
xgboost_model <-
parsnip::boost_tree(
mode = "classification",
trees = 100,
min_n = tune(),
tree_depth = tune(),
learn_rate = tune(),
loss_reduction = tune(),
mtry = tune()
) %>%
set_engine("xgboost") %>%
set_mode("classification")
xgboost_workflow <-
workflows::workflow() %>%
add_recipe(recipe) %>%
add_model(xgboost_model)
xgboost_grid <-
parameters(xgboost_model) %>%
finalize(select(training(mtcars_split), -drat)) %>%
grid_max_entropy(size = 100)
model_metrics <- yardstick::metric_set(gain_capture,roc_auc)
xgboost_tuned <-
tune::tune_grid(
object = xgboost_workflow,
resamples = cv_folds,
grid = xgboost_grid,
metrics = model_metrics,
control = tune::control_grid(save_pred = TRUE, save_workflow = TRUE)
)
xgboost_tuned$.notes
[[1]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[2]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[3]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
sessioninfo::session_info()
- Session info -------------------------------------------------------------------------------
setting value
version R version 4.0.5 (2021-03-31)
os Windows 10 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate Spanish_Spain.1252
ctype Spanish_Spain.1252
tz Europe/Paris
date 2021-06-25
- Packages -----------------------------------------------------------------------------------
package * version date lib source
askpass 1.1 2019-01-13 [1] CRAN (R 4.0.5)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.5)
backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.3)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.0.3)
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credentials 1.3.0 2020-07-21 [1] CRAN (R 4.0.5)
curl 4.3.1 2021-04-30 [1] CRAN (R 4.0.5)
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dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.0.5)
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fansi 0.4.2 2021-01-15 [1] CRAN (R 4.0.5)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.0.5)
fastmatch 1.1-0 2017-01-28 [1] CRAN (R 4.0.3)
FNN 1.1.3 2019-02-15 [1] CRAN (R 4.0.5)
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foreach 1.5.1 2020-10-15 [1] CRAN (R 4.0.5)
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fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.5)
furrr 0.2.2 2021-01-29 [1] CRAN (R 4.0.5)
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rsample * 0.1.0 2021-05-08 [1] CRAN (R 4.0.3)
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[1] C:/Users/Joe/R/win-library/4.0
[2] C:/Program Files/R/R-4.0.5/library
您确实将变量向量正确地传递给了配方——没问题！
您 运行 遇到了模型拟合的其他问题。 xgboost 模型要求所有预测变量都是数字，因此如果您将邮政编码之类的东西转换为因子，则需要使用 step_dummy()。如果您有诸如邮政编码之类的高基数，您可能还需要处理新关卡或未知关卡。
library(magrittr)
library(tidyverse)
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
mtcars1 <- mtcars
mtcars1 %<>% dplyr::mutate(new1 = sample.int(10, 32, replace = TRUE),
new2 = sample.int(5, 32, replace = TRUE))
my_categorical <- c("new1", "new2")
mtcars_split <- initial_split(mtcars1)
train <- training(mtcars_split)
test <- testing(mtcars_split)
cv_folds <- vfold_cv(train, v = 3)
rec <-
recipe(drat ~ ., data = train) %>%
step_mutate_at(all_of(my_categorical), fn = ~as.character(.)) %>%
step_string2factor(all_of(my_categorical)) %>%
step_novel(all_nominal_predictors()) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors())
xgboost_model <-
boost_tree(
mode = "classification",
trees = tune()
) %>%
set_engine("xgboost") %>%
set_mode("regression")
xgboost_workflow <-
workflow() %>%
add_recipe(rec) %>%
add_model(xgboost_model)
tune_grid(
object = xgboost_workflow,
resamples = cv_folds,
grid = 5
)
#> # Tuning results
#> # 3-fold cross-validation
#> # A tibble: 3 x 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [16/8]> Fold1 <tibble [10 × 5]> <tibble [0 × 1]>
#> 2 <split [16/8]> Fold2 <tibble [10 × 5]> <tibble [0 × 1]>
#> 3 <split [16/8]> Fold3 <tibble [10 × 5]> <tibble [0 × 1]>
由 reprex package (v2.0.0)
于 2021-06-25 创建
我不得不在你的例子中改变一些其他的东西来得到这个 运行，比如使用 "regression" 因为 drat 是数字等。我建议检查 reprex 包，因此您 运行 在新的 R 会话中像这样的示例并更有效地获得帮助。
我有一个训练模型的简单方法。我的分类变量随时间变化，有时我希望将数字视为分类变量（邮政编码），因此我在包含它们的配方之前定义了一个列表。 （只是为了论证，列表要长得多）
配方工作正常，然后训练我的模型（3 次）但出现错误。
model_tuned$.notes
[[1]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[2]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[3]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
是否有任何正确的方法将变量列表传递给配方而不会使模型崩溃？
REPREX
library(recipes)
library(magrittr)
library(tidyverse)
library(xgboost)
library(tidymodels)
mtcars1 <- mtcars
mtcars1 %<>% dplyr::mutate(new1 = sample.int(200, 32, replace = TRUE),
new2 = sample.int(100, 32, replace = TRUE),
new3 = sample.int(50, 32, replace = TRUE))
my_categorical <- c("new1", "new2", "new3")
mtcars_split <- initial_split(mtcars1, strata = drat)
train <- training(mtcars_split)
test <- testing(mtcars_split)
recipe <-
recipes::recipe(drat ~ ., data = train) %>%
recipes::step_mutate_at(all_of(my_categorical), fn = ~as.character(.)) %>%
recipes::step_string2factor(all_of(my_categorical)) %>%
prep()
cv_folds <-
vfold_cv(train,
v = 3,
strata = drat)
xgboost_model <-
parsnip::boost_tree(
mode = "classification",
trees = 100,
min_n = tune(),
tree_depth = tune(),
learn_rate = tune(),
loss_reduction = tune(),
mtry = tune()
) %>%
set_engine("xgboost") %>%
set_mode("classification")
xgboost_workflow <-
workflows::workflow() %>%
add_recipe(recipe) %>%
add_model(xgboost_model)
xgboost_grid <-
parameters(xgboost_model) %>%
finalize(select(training(mtcars_split), -drat)) %>%
grid_max_entropy(size = 100)
model_metrics <- yardstick::metric_set(gain_capture,roc_auc)
xgboost_tuned <-
tune::tune_grid(
object = xgboost_workflow,
resamples = cv_folds,
grid = xgboost_grid,
metrics = model_metrics,
control = tune::control_grid(save_pred = TRUE, save_workflow = TRUE)
)
xgboost_tuned$.notes
[[1]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[2]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
[[3]]
# A tibble: 1 x 1
.notes
<chr>
1 preprocessor 1/1: Error: object 'my_categorical' not found
sessioninfo::session_info()
- Session info -------------------------------------------------------------------------------
setting value
version R version 4.0.5 (2021-03-31)
os Windows 10 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate Spanish_Spain.1252
ctype Spanish_Spain.1252
tz Europe/Paris
date 2021-06-25
- Packages -----------------------------------------------------------------------------------
package * version date lib source
askpass 1.1 2019-01-13 [1] CRAN (R 4.0.5)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.5)
backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.3)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.0.3)
BBmisc 1.11 2017-03-10 [1] CRAN (R 4.0.5)
broom * 0.7.6 2021-04-05 [1] CRAN (R 4.0.5)
butcher 0.1.4 2021-03-19 [1] CRAN (R 4.0.5)
cachem 1.0.4 2021-02-13 [1] CRAN (R 4.0.5)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.5)
checkmate 2.0.0 2020-02-06 [1] CRAN (R 4.0.5)
class 7.3-18 2021-01-24 [2] CRAN (R 4.0.5)
cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.5)
cluster 2.1.1 2021-02-14 [2] CRAN (R 4.0.5)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.0.5)
colorspace 2.0-1 2021-05-04 [1] CRAN (R 4.0.5)
crayon 1.4.1 2021-02-08 [1] CRAN (R 4.0.5)
credentials 1.3.0 2020-07-21 [1] CRAN (R 4.0.5)
curl 4.3.1 2021-04-30 [1] CRAN (R 4.0.5)
data.table 1.14.0 2021-02-21 [1] CRAN (R 4.0.5)
DBI 1.1.1 2021-01-15 [1] CRAN (R 4.0.5)
dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.0.5)
dials * 0.0.9 2020-09-16 [1] CRAN (R 4.0.5)
DiceDesign 1.9 2021-02-13 [1] CRAN (R 4.0.5)
digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.5)
doParallel 1.0.16 2020-10-16 [1] CRAN (R 4.0.5)
dplyr * 1.0.6 2021-05-05 [1] CRAN (R 4.0.3)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.0.5)
fansi 0.4.2 2021-01-15 [1] CRAN (R 4.0.5)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.0.5)
fastmatch 1.1-0 2017-01-28 [1] CRAN (R 4.0.3)
FNN 1.1.3 2019-02-15 [1] CRAN (R 4.0.5)
forcats * 0.5.1 2021-01-27 [1] CRAN (R 4.0.5)
foreach 1.5.1 2020-10-15 [1] CRAN (R 4.0.5)
foreign 0.8-81 2020-12-22 [2] CRAN (R 4.0.5)
Formula 1.2-4 2020-10-16 [1] CRAN (R 4.0.3)
fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.5)
furrr 0.2.2 2021-01-29 [1] CRAN (R 4.0.5)
future 1.21.0 2020-12-10 [1] CRAN (R 4.0.5)
generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.5)
gert 1.3.0 2021-03-29 [1] CRAN (R 4.0.5)
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Matrix 1.3-2 2021-01-06 [2] CRAN (R 4.0.5)
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parsnip * 0.1.5 2021-01-19 [1] CRAN (R 4.0.5)
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[1] C:/Users/Joe/R/win-library/4.0
[2] C:/Program Files/R/R-4.0.5/library
您确实将变量向量正确地传递给了配方——没问题！
您 运行 遇到了模型拟合的其他问题。 xgboost 模型要求所有预测变量都是数字，因此如果您将邮政编码之类的东西转换为因子，则需要使用 step_dummy()。如果您有诸如邮政编码之类的高基数，您可能还需要处理新关卡或未知关卡。
library(magrittr)
library(tidyverse)
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
mtcars1 <- mtcars
mtcars1 %<>% dplyr::mutate(new1 = sample.int(10, 32, replace = TRUE),
new2 = sample.int(5, 32, replace = TRUE))
my_categorical <- c("new1", "new2")
mtcars_split <- initial_split(mtcars1)
train <- training(mtcars_split)
test <- testing(mtcars_split)
cv_folds <- vfold_cv(train, v = 3)
rec <-
recipe(drat ~ ., data = train) %>%
step_mutate_at(all_of(my_categorical), fn = ~as.character(.)) %>%
step_string2factor(all_of(my_categorical)) %>%
step_novel(all_nominal_predictors()) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors())
xgboost_model <-
boost_tree(
mode = "classification",
trees = tune()
) %>%
set_engine("xgboost") %>%
set_mode("regression")
xgboost_workflow <-
workflow() %>%
add_recipe(rec) %>%
add_model(xgboost_model)
tune_grid(
object = xgboost_workflow,
resamples = cv_folds,
grid = 5
)
#> # Tuning results
#> # 3-fold cross-validation
#> # A tibble: 3 x 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [16/8]> Fold1 <tibble [10 × 5]> <tibble [0 × 1]>
#> 2 <split [16/8]> Fold2 <tibble [10 × 5]> <tibble [0 × 1]>
#> 3 <split [16/8]> Fold3 <tibble [10 × 5]> <tibble [0 × 1]>
由 reprex package (v2.0.0)
于 2021-06-25 创建我不得不在你的例子中改变一些其他的东西来得到这个 运行，比如使用 "regression" 因为 drat 是数字等。我建议检查 reprex 包，因此您 运行 在新的 R 会话中像这样的示例并更有效地获得帮助。