Tidymodels: Decision Tree Learning in R - Error: No variables or terms were selected
Tidymodels: Decision Tree Learning in R - Error: No variables or terms were selected
概览：
我有一个名为“FID”的数据框，我正在尝试按照本教程（见下文）生成三个模型：(1) 袋装树； (2) 随机森林；和 (3) 提升树。
教程：
https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/
问题
当我尝试 运行 模型时，"fit_bag"、fit_rf , "fit_boost", 我在下面遇到此错误消息。我认为问题可能出在预处理阶段。
有人可以就此问题提供建议吗？
非常感谢。
错误 - 未选择任何变量
i Fold01: recipe
x Fold01: recipe: Error: No variables or terms were selected.
i Fold02: recipe
x Fold02: recipe: Error: No variables or terms were selected.
i Fold03: recipe
x Fold03: recipe: Error: No variables or terms were selected.
i Fold04: recipe
x Fold04: recipe: Error: No variables or terms were selected.
i Fold05: recipe
x Fold05: recipe: Error: No variables or terms were selected.
i Fold06: recipe
x Fold06: recipe: Error: No variables or terms were selected.
i Fold07: recipe
x Fold07: recipe: Error: No variables or terms were selected.
i Fold08: recipe
x Fold08: recipe: Error: No variables or terms were selected.
i Fold09: recipe
x Fold09: recipe: Error: No variables or terms were selected.
i Fold10: recipe
x Fold10: recipe: Error: No variables or terms were selected.
Warning message:
All models failed in [fit_resamples()]. See the `.notes` column.
R-code
##Open library packages
library(tidymodels)
library(tidyverse) # manipulating data
library(skimr) # data visualization
library(baguette) # bagged trees
library(future) # parallel processing & decrease computation time
library(xgboost) # boosted trees
# split the data
split <- initial_split(Tidmodel_df)
# extract the training data
train <- training(split)
# resample the data with 10-fold cross-validation (10-fold by default)
cv <- vfold_cv(train)
##Preprocessing
rec <- recipe(Frequency~., data=train) %>%
update_role(contains("id"), Year, Month, Monsoon, Days, new_role = "id vars") %>% # declare ID variables
step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels
step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars")) %>% # replaces missing numeric observations with the median
step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables
###########################################################
##Create Models
###########################################################
#####Bagged Trees
mod_bag <- bag_tree() %>%
set_mode("regression") %>%
set_engine("rpart", times = 10) # 10 bootstrap resamples
##Create workflow
wflow_bag <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_bag)
##Fit the model
plan(multisession)
fit_bag <- fit_resamples(
wflow_bag,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE,
extract = function(x) extract_model(x)))
##Random forests
mod_rf <-rand_forest() %>%
set_engine("ranger",
num.threads = parallel::detectCores(),
importance = "permutation",
verbose = TRUE) %>%
set_mode("regression") %>%
set_args(trees = 1000)
##Create Workflow
wflow_rf <- workflow() %>%
add_model(mod_rf) %>%
add_recipe(rec)
##Fit the model
plan(multisession)
fit_rf <- fit_resamples(
wflow_rf,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE,
extract = function(x) x)
)
##Boosted Trees
mod_boost <- boost_tree() %>%
set_engine("xgboost", nthreads = parallel::detectCores()) %>%
set_mode("regression")
##Create workflow
wflow_boost <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_boost)
##Fit model
plan(multisession)
fit_boost <- fit_resamples(
wflow_boost,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE)
)
数据框 - FID
structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March",
"April", "May", "June", "July", "August", "September", "October",
"November", "December"), class = "factor"), Monsoon = structure(c(2L,
2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 2L), .Label = c("First_Inter_Monssoon", "North_Monsoon",
"Second_Inter_Monsoon", "South_Monsson"), class = "factor"),
Frequency = c(36, 28, 39, 46, 5, 0, 0, 22, 10, 15, 8,
33, 33, 29, 31, 23, 8, 9, 7, 40, 41, 41, 30, 30, 44, 37,
41, 42, 20, 0, 7, 27, 35, 27, 43, 38), Days = c(31,
28, 31, 30, 6, 0, 0, 29, 15, 29, 29, 31, 31, 29, 30, 30,
7, 0, 7, 30, 30, 31, 30, 27, 31, 28, 30, 30, 21, 0, 7, 26,
29, 27, 29, 29)), row.names = c(NA, -36L), class = "data.frame")
这里的问题是，当你使用 update_role(contains("id"), Year, Month, Monsoon, Days, new_role = "id vars") 时，你将 Year、Month、Monsoon 等所有变量的角色更新为 "id vars" 然后他们就不再是预测变量了。当配方进入下一个预处理步骤时，它发现根本没有任何预测变量。
如果您想将这些变量用作预测变量，请保持它们的作用不变，不要将它们更改为其他变量，例如 "id vars":
library(tidymodels)
library(baguette) # bagged trees
fid_df <- structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March",
"April", "May", "June", "July", "August", "September", "October",
"November", "December"), class = "factor"), Monsoon = structure(c(2L,
2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 2L), .Label = c("First_Inter_Monssoon", "North_Monsoon",
"Second_Inter_Monsoon", "South_Monsson"), class = "factor"),
Frequency = c(36, 28, 39, 46, 5, 0, 0, 22, 10, 15, 8,
33, 33, 29, 31, 23, 8, 9, 7, 40, 41, 41, 30, 30, 44, 37,
41, 42, 20, 0, 7, 27, 35, 27, 43, 38), Days = c(31,
28, 31, 30, 6, 0, 0, 29, 15, 29, 29, 31, 31, 29, 30, 30,
7, 0, 7, 30, 30, 31, 30, 27, 31, 28, 30, 30, 21, 0, 7, 26,
29, 27, 29, 29)), row.names = c(NA, -36L), class = "data.frame")
# split the data
fid_split <- initial_split(fid_df)
# extract the training data
fid_train <- training(fid_split)
# resample the data with 10-fold cross-validation (10-fold by default)
cv <- vfold_cv(fid_train)
##Preprocessing
rec <- recipe(Frequency ~ ., data = fid_df) %>%
step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels
step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars")) %>% # replaces missing numeric observations with the median
step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables
rf_spec <- rand_forest(trees = 1e3) %>%
set_engine("ranger", importance = "permutation") %>%
set_mode("regression")
wflow_rf <- workflow() %>%
add_model(rf_spec) %>%
add_recipe(rec)
fit_resamples(
wflow_rf,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE)
)
#>
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#>
#> %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
#> flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
#> splice
#>
#> Attaching package: 'vctrs'
#> The following object is masked from 'package:tibble':
#>
#> data_frame
#> The following object is masked from 'package:dplyr':
#>
#> data_frame
#> # Resampling results
#> # 10-fold cross-validation
#> # A tibble: 10 x 5
#> splits id .metrics .notes .predictions
#> <list> <chr> <list> <list> <list>
#> 1 <split [24/3]> Fold01 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 2 <split [24/3]> Fold02 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 3 <split [24/3]> Fold03 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 4 <split [24/3]> Fold04 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 5 <split [24/3]> Fold05 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 6 <split [24/3]> Fold06 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 7 <split [24/3]> Fold07 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 8 <split [25/2]> Fold08 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [2 × 4]>
#> 9 <split [25/2]> Fold09 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [2 × 4]>
#> 10 <split [25/2]> Fold10 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [2 × 4]>
由 reprex package (v0.3.0.9001)
于 2020-11-18 创建
概览：
我有一个名为“FID”的数据框，我正在尝试按照本教程（见下文）生成三个模型：(1) 袋装树； (2) 随机森林；和 (3) 提升树。
教程：
https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/
问题
当我尝试 运行 模型时，"fit_bag"、fit_rf , "fit_boost", 我在下面遇到此错误消息。我认为问题可能出在预处理阶段。
有人可以就此问题提供建议吗？
非常感谢。
错误 - 未选择任何变量
i Fold01: recipe
x Fold01: recipe: Error: No variables or terms were selected.
i Fold02: recipe
x Fold02: recipe: Error: No variables or terms were selected.
i Fold03: recipe
x Fold03: recipe: Error: No variables or terms were selected.
i Fold04: recipe
x Fold04: recipe: Error: No variables or terms were selected.
i Fold05: recipe
x Fold05: recipe: Error: No variables or terms were selected.
i Fold06: recipe
x Fold06: recipe: Error: No variables or terms were selected.
i Fold07: recipe
x Fold07: recipe: Error: No variables or terms were selected.
i Fold08: recipe
x Fold08: recipe: Error: No variables or terms were selected.
i Fold09: recipe
x Fold09: recipe: Error: No variables or terms were selected.
i Fold10: recipe
x Fold10: recipe: Error: No variables or terms were selected.
Warning message:
All models failed in [fit_resamples()]. See the `.notes` column.
R-code
##Open library packages
library(tidymodels)
library(tidyverse) # manipulating data
library(skimr) # data visualization
library(baguette) # bagged trees
library(future) # parallel processing & decrease computation time
library(xgboost) # boosted trees
# split the data
split <- initial_split(Tidmodel_df)
# extract the training data
train <- training(split)
# resample the data with 10-fold cross-validation (10-fold by default)
cv <- vfold_cv(train)
##Preprocessing
rec <- recipe(Frequency~., data=train) %>%
update_role(contains("id"), Year, Month, Monsoon, Days, new_role = "id vars") %>% # declare ID variables
step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels
step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars")) %>% # replaces missing numeric observations with the median
step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables
###########################################################
##Create Models
###########################################################
#####Bagged Trees
mod_bag <- bag_tree() %>%
set_mode("regression") %>%
set_engine("rpart", times = 10) # 10 bootstrap resamples
##Create workflow
wflow_bag <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_bag)
##Fit the model
plan(multisession)
fit_bag <- fit_resamples(
wflow_bag,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE,
extract = function(x) extract_model(x)))
##Random forests
mod_rf <-rand_forest() %>%
set_engine("ranger",
num.threads = parallel::detectCores(),
importance = "permutation",
verbose = TRUE) %>%
set_mode("regression") %>%
set_args(trees = 1000)
##Create Workflow
wflow_rf <- workflow() %>%
add_model(mod_rf) %>%
add_recipe(rec)
##Fit the model
plan(multisession)
fit_rf <- fit_resamples(
wflow_rf,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE,
extract = function(x) x)
)
##Boosted Trees
mod_boost <- boost_tree() %>%
set_engine("xgboost", nthreads = parallel::detectCores()) %>%
set_mode("regression")
##Create workflow
wflow_boost <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_boost)
##Fit model
plan(multisession)
fit_boost <- fit_resamples(
wflow_boost,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(verbose = TRUE,
save_pred = TRUE)
)
数据框 - FID
structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March",
"April", "May", "June", "July", "August", "September", "October",
"November", "December"), class = "factor"), Monsoon = structure(c(2L,
2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 2L), .Label = c("First_Inter_Monssoon", "North_Monsoon",
"Second_Inter_Monsoon", "South_Monsson"), class = "factor"),
Frequency = c(36, 28, 39, 46, 5, 0, 0, 22, 10, 15, 8,
33, 33, 29, 31, 23, 8, 9, 7, 40, 41, 41, 30, 30, 44, 37,
41, 42, 20, 0, 7, 27, 35, 27, 43, 38), Days = c(31,
28, 31, 30, 6, 0, 0, 29, 15, 29, 29, 31, 31, 29, 30, 30,
7, 0, 7, 30, 30, 31, 30, 27, 31, 28, 30, 30, 21, 0, 7, 26,
29, 27, 29, 29)), row.names = c(NA, -36L), class = "data.frame")
这里的问题是，当你使用 update_role(contains("id"), Year, Month, Monsoon, Days, new_role = "id vars") 时，你将 Year、Month、Monsoon 等所有变量的角色更新为 "id vars" 然后他们就不再是预测变量了。当配方进入下一个预处理步骤时，它发现根本没有任何预测变量。
如果您想将这些变量用作预测变量，请保持它们的作用不变，不要将它们更改为其他变量，例如 "id vars":
library(tidymodels)
library(baguette) # bagged trees
fid_df <- structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March",
"April", "May", "June", "July", "August", "September", "October",
"November", "December"), class = "factor"), Monsoon = structure(c(2L,
2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 2L), .Label = c("First_Inter_Monssoon", "North_Monsoon",
"Second_Inter_Monsoon", "South_Monsson"), class = "factor"),
Frequency = c(36, 28, 39, 46, 5, 0, 0, 22, 10, 15, 8,
33, 33, 29, 31, 23, 8, 9, 7, 40, 41, 41, 30, 30, 44, 37,
41, 42, 20, 0, 7, 27, 35, 27, 43, 38), Days = c(31,
28, 31, 30, 6, 0, 0, 29, 15, 29, 29, 31, 31, 29, 30, 30,
7, 0, 7, 30, 30, 31, 30, 27, 31, 28, 30, 30, 21, 0, 7, 26,
29, 27, 29, 29)), row.names = c(NA, -36L), class = "data.frame")
# split the data
fid_split <- initial_split(fid_df)
# extract the training data
fid_train <- training(fid_split)
# resample the data with 10-fold cross-validation (10-fold by default)
cv <- vfold_cv(fid_train)
##Preprocessing
rec <- recipe(Frequency ~ ., data = fid_df) %>%
step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels
step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars")) %>% # replaces missing numeric observations with the median
step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables
rf_spec <- rand_forest(trees = 1e3) %>%
set_engine("ranger", importance = "permutation") %>%
set_mode("regression")
wflow_rf <- workflow() %>%
add_model(rf_spec) %>%
add_recipe(rec)
fit_resamples(
wflow_rf,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE)
)
#>
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#>
#> %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
#> flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
#> splice
#>
#> Attaching package: 'vctrs'
#> The following object is masked from 'package:tibble':
#>
#> data_frame
#> The following object is masked from 'package:dplyr':
#>
#> data_frame
#> # Resampling results
#> # 10-fold cross-validation
#> # A tibble: 10 x 5
#> splits id .metrics .notes .predictions
#> <list> <chr> <list> <list> <list>
#> 1 <split [24/3]> Fold01 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 2 <split [24/3]> Fold02 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 3 <split [24/3]> Fold03 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 4 <split [24/3]> Fold04 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 5 <split [24/3]> Fold05 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 6 <split [24/3]> Fold06 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 7 <split [24/3]> Fold07 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [3 × 4]>
#> 8 <split [25/2]> Fold08 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [2 × 4]>
#> 9 <split [25/2]> Fold09 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [2 × 4]>
#> 10 <split [25/2]> Fold10 <tibble [2 × 4]> <tibble [0 × 1]> <tibble [2 × 4]>
由 reprex package (v0.3.0.9001)
于 2020-11-18 创建