fit_resamples 使用 ranger 包失败
fit_resamples with ranger package fails
尝试使用交叉折叠重采样并从 ranger 包中拟合随机森林。没有重新采样的拟合有效，但是一旦我尝试重新采样拟合，它就会失败并出现以下错误。
考虑关注 df
df<-structure(list(a = c(1379405931, 732812609, 18614430, 1961678341,
2362202769, 55687714, 72044715, 236503454, 61988734, 2524712675,
98081131, 1366513385, 48203585, 697397991, 28132854), b = structure(c(1L,
6L, 2L, 5L, 7L, 8L, 8L, 1L, 3L, 4L, 3L, 5L, 7L, 2L, 2L), .Label = c("CA",
"IA", "IL", "LA", "MA", "MN", "TX", "WI"), class = "factor"),
c = structure(c(2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L), .Label = c("R", "U"), class = "factor"),
d = structure(c(3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L,
3L, 2L, 3L, 1L), .Label = c("CAH", "LTCH", "STH"), class = "factor"),
e = structure(c(3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 4L, 2L,
2L, 3L, 3L, 3L), .Label = c("cancer", "general long term",
"psychiatric", "rehabilitation"), class = "factor")), row.names = c(NA,
-15L), class = c("tbl_df", "tbl", "data.frame"))
以下简单拟合工作没有问题
library(tidymodels)
library(ranger)
rf_spec <- rand_forest(mode = 'regression') %>%
set_engine('ranger')
rf_spec %>%
fit(a ~. , data = df)
但是一旦我想 运行 通过
进行交叉验证
rf_folds <- vfold_cv(df, strata = c)
fit_resamples(a ~ . ,
rf_spec,
rf_folds)
跟随误差
model: Error in parse.formula(formula, data, env = parent.frame()): Error: Illegal column names in formula interface. Fix column names or use alternative interface in ranger.
上面的评论者是正确的，这里的问题根源是因子列中的空格。重采样函数和普通旧拟合函数目前处理方式不同，我们正在积极研究如何为用户解决这个问题。感谢您的耐心等待！
同时，我建议设置一个简单的 workflow() plus a recipe()，它们一起将为您处理所有必要的虚拟变量修改。
library(tidymodels)
rf_spec <- rand_forest(mode = "regression") %>%
set_engine("ranger")
rf_wf <- workflow() %>%
add_model(rf_spec) %>%
add_recipe(recipe(a ~ ., data = df))
fit(rf_wf, data = df)
#> ══ Workflow [trained] ═══════════════════════════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: rand_forest()
#>
#> ── Preprocessor ─────────────────────────────────────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#>
#> ── Model ────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Ranger result
#>
#> Call:
#> ranger::ranger(formula = formula, data = data, num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
#>
#> Type: Regression
#> Number of trees: 500
#> Sample size: 15
#> Number of independent variables: 4
#> Mtry: 2
#> Target node size: 5
#> Variable importance mode: none
#> Splitrule: variance
#> OOB prediction error (MSE): 4.7042e+17
#> R squared (OOB): 0.4341146
rf_folds <- vfold_cv(df, strata = c)
fit_resamples(rf_wf,
rf_folds)
#> # 10-fold cross-validation using stratification
#> # A tibble: 9 x 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [13/2]> Fold1 <tibble [2 × 3]> <tibble [0 × 1]>
#> 2 <split [13/2]> Fold2 <tibble [2 × 3]> <tibble [0 × 1]>
#> 3 <split [13/2]> Fold3 <tibble [2 × 3]> <tibble [0 × 1]>
#> 4 <split [13/2]> Fold4 <tibble [2 × 3]> <tibble [0 × 1]>
#> 5 <split [13/2]> Fold5 <tibble [2 × 3]> <tibble [0 × 1]>
#> 6 <split [13/2]> Fold6 <tibble [2 × 3]> <tibble [0 × 1]>
#> 7 <split [14/1]> Fold7 <tibble [2 × 3]> <tibble [0 × 1]>
#> 8 <split [14/1]> Fold8 <tibble [2 × 3]> <tibble [0 × 1]>
#> 9 <split [14/1]> Fold9 <tibble [2 × 3]> <tibble [0 × 1]>
由 reprex package (v0.3.0)
于 2020 年 3 月 20 日创建
Julia 和我打赌所以她得到了业力。我有同样的答案（我做她做的但速度较慢）：
这是 种 错误，我们一直在努力以最佳方式使其不再出错。情况很复杂。让我解释。
ranger 是少数几个公式方法不创建虚拟变量的 R 包之一（明智的是因为它不需要它们）。
tune 中的基础设施使用 workflows 包来处理公式，然后将结果数据交给 ranger。 默认情况下，工作流会创建虚拟变量，并且由于您的某些因子水平不是有效的 R 列名称（例如 "general long term"），ranger() 会引发错误。
（我知道您没有使用工作流程，但这就是幕后发生的事情）。
我们正在努力做最好的事情，因为大多数用户不知道许多基于树的模型包不会产生虚拟变量。更复杂的是，parsnip 还没有使用工作流，也没有给你一个错误。
暂时解决
使用简单的配方代替公式：
library(tidymodels)
#> ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────── tidymodels 0.1.0 ──
#> ✓ broom 0.5.4 ✓ recipes 0.1.10
#> ✓ dials 0.0.4 ✓ rsample 0.0.5
#> ✓ dplyr 0.8.5 ✓ tibble 2.1.3
#> ✓ ggplot2 3.3.0 ✓ tune 0.0.1
#> ✓ infer 0.5.1 ✓ workflows 0.1.0
#> ✓ parsnip 0.0.5 ✓ yardstick 0.0.5
#> ✓ purrr 0.3.3
#> ── Conflicts ────────────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x ggplot2::margin() masks dials::margin()
#> x recipes::step() masks stats::step()
df<-structure(list(a = c(1379405931, 732812609, 18614430, 1961678341,
2362202769, 55687714, 72044715, 236503454, 61988734, 2524712675,
98081131, 1366513385, 48203585, 697397991, 28132854), b = structure(c(1L,
6L, 2L, 5L, 7L, 8L, 8L, 1L, 3L, 4L, 3L, 5L, 7L, 2L, 2L), .Label = c("CA",
"IA", "IL", "LA", "MA", "MN", "TX", "WI"), class = "factor"),
c = structure(c(2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L), .Label = c("R", "U"), class = "factor"),
d = structure(c(3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L,
3L, 2L, 3L, 1L), .Label = c("CAH", "LTCH", "STH"), class = "factor"),
e = structure(c(3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 4L, 2L,
2L, 3L, 3L, 3L), .Label = c("cancer", "general long term",
"psychiatric", "rehabilitation"), class = "factor")), row.names = c(NA,
-15L), class = c("tbl_df", "tbl", "data.frame"))
library(tidymodels)
library(ranger)
rf_spec <- rand_forest(mode = 'regression') %>%
set_engine('ranger')
rf_folds <- vfold_cv(df, strata = c)
fit_resamples(recipe(a ~ ., data = df), rf_spec, rf_folds)
#> # 10-fold cross-validation using stratification
#> # A tibble: 9 x 4
#> splits id .metrics .notes
#> * <list> <chr> <list> <list>
#> 1 <split [13/2]> Fold1 <tibble [2 × 3]> <tibble [0 × 1]>
#> 2 <split [13/2]> Fold2 <tibble [2 × 3]> <tibble [0 × 1]>
#> 3 <split [13/2]> Fold3 <tibble [2 × 3]> <tibble [0 × 1]>
#> 4 <split [13/2]> Fold4 <tibble [2 × 3]> <tibble [0 × 1]>
#> 5 <split [13/2]> Fold5 <tibble [2 × 3]> <tibble [0 × 1]>
#> 6 <split [13/2]> Fold6 <tibble [2 × 3]> <tibble [0 × 1]>
#> 7 <split [14/1]> Fold7 <tibble [2 × 3]> <tibble [0 × 1]>
#> 8 <split [14/1]> Fold8 <tibble [2 × 3]> <tibble [0 × 1]>
#> 9 <split [14/1]> Fold9 <tibble [2 × 3]> <tibble [0 × 1]>
# FYI `tune` 0.0.2 will require a different argument order:
# rf_spec %>% fit_resamples(recipe(a ~ ., data = df), rf_folds)
由 reprex package (v0.3.0)
于 2020 年 3 月 20 日创建
尝试使用交叉折叠重采样并从 ranger 包中拟合随机森林。没有重新采样的拟合有效，但是一旦我尝试重新采样拟合，它就会失败并出现以下错误。
考虑关注 df
df<-structure(list(a = c(1379405931, 732812609, 18614430, 1961678341,
2362202769, 55687714, 72044715, 236503454, 61988734, 2524712675,
98081131, 1366513385, 48203585, 697397991, 28132854), b = structure(c(1L,
6L, 2L, 5L, 7L, 8L, 8L, 1L, 3L, 4L, 3L, 5L, 7L, 2L, 2L), .Label = c("CA",
"IA", "IL", "LA", "MA", "MN", "TX", "WI"), class = "factor"),
c = structure(c(2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L), .Label = c("R", "U"), class = "factor"),
d = structure(c(3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L,
3L, 2L, 3L, 1L), .Label = c("CAH", "LTCH", "STH"), class = "factor"),
e = structure(c(3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 4L, 2L,
2L, 3L, 3L, 3L), .Label = c("cancer", "general long term",
"psychiatric", "rehabilitation"), class = "factor")), row.names = c(NA,
-15L), class = c("tbl_df", "tbl", "data.frame"))
以下简单拟合工作没有问题
library(tidymodels)
library(ranger)
rf_spec <- rand_forest(mode = 'regression') %>%
set_engine('ranger')
rf_spec %>%
fit(a ~. , data = df)
但是一旦我想 运行 通过
进行交叉验证rf_folds <- vfold_cv(df, strata = c)
fit_resamples(a ~ . ,
rf_spec,
rf_folds)
跟随误差
model: Error in parse.formula(formula, data, env = parent.frame()): Error: Illegal column names in formula interface. Fix column names or use alternative interface in ranger.
上面的评论者是正确的，这里的问题根源是因子列中的空格。重采样函数和普通旧拟合函数目前处理方式不同，我们正在积极研究如何为用户解决这个问题。感谢您的耐心等待！
同时，我建议设置一个简单的 workflow() plus a recipe()，它们一起将为您处理所有必要的虚拟变量修改。
library(tidymodels)
rf_spec <- rand_forest(mode = "regression") %>%
set_engine("ranger")
rf_wf <- workflow() %>%
add_model(rf_spec) %>%
add_recipe(recipe(a ~ ., data = df))
fit(rf_wf, data = df)
#> ══ Workflow [trained] ═══════════════════════════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: rand_forest()
#>
#> ── Preprocessor ─────────────────────────────────────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#>
#> ── Model ────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Ranger result
#>
#> Call:
#> ranger::ranger(formula = formula, data = data, num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
#>
#> Type: Regression
#> Number of trees: 500
#> Sample size: 15
#> Number of independent variables: 4
#> Mtry: 2
#> Target node size: 5
#> Variable importance mode: none
#> Splitrule: variance
#> OOB prediction error (MSE): 4.7042e+17
#> R squared (OOB): 0.4341146
rf_folds <- vfold_cv(df, strata = c)
fit_resamples(rf_wf,
rf_folds)
#> # 10-fold cross-validation using stratification
#> # A tibble: 9 x 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [13/2]> Fold1 <tibble [2 × 3]> <tibble [0 × 1]>
#> 2 <split [13/2]> Fold2 <tibble [2 × 3]> <tibble [0 × 1]>
#> 3 <split [13/2]> Fold3 <tibble [2 × 3]> <tibble [0 × 1]>
#> 4 <split [13/2]> Fold4 <tibble [2 × 3]> <tibble [0 × 1]>
#> 5 <split [13/2]> Fold5 <tibble [2 × 3]> <tibble [0 × 1]>
#> 6 <split [13/2]> Fold6 <tibble [2 × 3]> <tibble [0 × 1]>
#> 7 <split [14/1]> Fold7 <tibble [2 × 3]> <tibble [0 × 1]>
#> 8 <split [14/1]> Fold8 <tibble [2 × 3]> <tibble [0 × 1]>
#> 9 <split [14/1]> Fold9 <tibble [2 × 3]> <tibble [0 × 1]>
由 reprex package (v0.3.0)
于 2020 年 3 月 20 日创建Julia 和我打赌所以她得到了业力。我有同样的答案（我做她做的但速度较慢）：
这是 种 错误，我们一直在努力以最佳方式使其不再出错。情况很复杂。让我解释。
ranger 是少数几个公式方法不创建虚拟变量的 R 包之一（明智的是因为它不需要它们）。
tune 中的基础设施使用 workflows 包来处理公式，然后将结果数据交给 ranger。 默认情况下，工作流会创建虚拟变量，并且由于您的某些因子水平不是有效的 R 列名称（例如 "general long term"），ranger() 会引发错误。
（我知道您没有使用工作流程，但这就是幕后发生的事情）。
我们正在努力做最好的事情，因为大多数用户不知道许多基于树的模型包不会产生虚拟变量。更复杂的是，parsnip 还没有使用工作流，也没有给你一个错误。
暂时解决
使用简单的配方代替公式：
library(tidymodels)
#> ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────── tidymodels 0.1.0 ──
#> ✓ broom 0.5.4 ✓ recipes 0.1.10
#> ✓ dials 0.0.4 ✓ rsample 0.0.5
#> ✓ dplyr 0.8.5 ✓ tibble 2.1.3
#> ✓ ggplot2 3.3.0 ✓ tune 0.0.1
#> ✓ infer 0.5.1 ✓ workflows 0.1.0
#> ✓ parsnip 0.0.5 ✓ yardstick 0.0.5
#> ✓ purrr 0.3.3
#> ── Conflicts ────────────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x ggplot2::margin() masks dials::margin()
#> x recipes::step() masks stats::step()
df<-structure(list(a = c(1379405931, 732812609, 18614430, 1961678341,
2362202769, 55687714, 72044715, 236503454, 61988734, 2524712675,
98081131, 1366513385, 48203585, 697397991, 28132854), b = structure(c(1L,
6L, 2L, 5L, 7L, 8L, 8L, 1L, 3L, 4L, 3L, 5L, 7L, 2L, 2L), .Label = c("CA",
"IA", "IL", "LA", "MA", "MN", "TX", "WI"), class = "factor"),
c = structure(c(2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L), .Label = c("R", "U"), class = "factor"),
d = structure(c(3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L,
3L, 2L, 3L, 1L), .Label = c("CAH", "LTCH", "STH"), class = "factor"),
e = structure(c(3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 4L, 2L,
2L, 3L, 3L, 3L), .Label = c("cancer", "general long term",
"psychiatric", "rehabilitation"), class = "factor")), row.names = c(NA,
-15L), class = c("tbl_df", "tbl", "data.frame"))
library(tidymodels)
library(ranger)
rf_spec <- rand_forest(mode = 'regression') %>%
set_engine('ranger')
rf_folds <- vfold_cv(df, strata = c)
fit_resamples(recipe(a ~ ., data = df), rf_spec, rf_folds)
#> # 10-fold cross-validation using stratification
#> # A tibble: 9 x 4
#> splits id .metrics .notes
#> * <list> <chr> <list> <list>
#> 1 <split [13/2]> Fold1 <tibble [2 × 3]> <tibble [0 × 1]>
#> 2 <split [13/2]> Fold2 <tibble [2 × 3]> <tibble [0 × 1]>
#> 3 <split [13/2]> Fold3 <tibble [2 × 3]> <tibble [0 × 1]>
#> 4 <split [13/2]> Fold4 <tibble [2 × 3]> <tibble [0 × 1]>
#> 5 <split [13/2]> Fold5 <tibble [2 × 3]> <tibble [0 × 1]>
#> 6 <split [13/2]> Fold6 <tibble [2 × 3]> <tibble [0 × 1]>
#> 7 <split [14/1]> Fold7 <tibble [2 × 3]> <tibble [0 × 1]>
#> 8 <split [14/1]> Fold8 <tibble [2 × 3]> <tibble [0 × 1]>
#> 9 <split [14/1]> Fold9 <tibble [2 × 3]> <tibble [0 × 1]>
# FYI `tune` 0.0.2 will require a different argument order:
# rf_spec %>% fit_resamples(recipe(a ~ ., data = df), rf_folds)
由 reprex package (v0.3.0)
于 2020 年 3 月 20 日创建