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我想知道我在recipe下面做错了什么reprex。它似乎在所有超参数上产生了一个平坦的 MAE。我仍在学习如何使用tidymodels，所以也许我在这里缺少学习技巧，但是伸出手看看是否有经验丰富的人可以在这里帮助指导我。谢谢！

library(tidymodels)
#> ── Attaching packages ──────────────────────────── tidymodels 0.1.1 ──
#> ✓ broom     0.7.0      ✓ recipes   0.1.13
#> ✓ dials     0.0.8      ✓ rsample   0.0.7 
#> ✓ dplyr     1.0.2      ✓ tibble    3.0.3 
#> ✓ ggplot2   3.3.2      ✓ tidyr     1.1.2 
#> ✓ infer     0.5.3      ✓ tune      0.1.1 
#> ✓ modeldata 0.0.2      ✓ workflows 0.1.3 
#> ✓ parsnip   0.1.3      ✓ 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()

data(mtcars)

set.seed(101)
clean_split <- initial_split(mtcars, prop = 0.8)

train <- training(clean_split)
test <- testing(clean_split)


recipe <- recipe(mpg ~ ., data = train) %>%
       step_log(all_outcomes(), base = 10, skip = TRUE) %>%
       step_center(all_predictors(), -all_nominal()) %>%
       step_scale(all_predictors(), -all_nominal()) %>%
       step_other(all_nominal(), threshold = 0.01) %>%
       step_dummy(all_nominal()) %>% 
       step_zv(all_predictors(), -all_outcomes())

### Creating models
#### GLMNET
glmnet_model <- linear_reg(mode = "regression",
                           penalty = tune(),
                           mixture = tune()) %>%
       set_engine("glmnet")

glmnet_model
#> Linear Regression Model Specification (regression)
#> 
#> Main Arguments:
#>   penalty = tune()
#>   mixture = tune()
#> 
#> Computational engine: glmnet

glmnet_params <- parameters(penalty(), mixture())
glmnet_params
#> Collection of 2 parameters for tuning
#> 
#>       id parameter type object class
#>  penalty        penalty    nparam[+]
#>  mixture        mixture    nparam[+]

set.seed(123)
glmnet_grid <- grid_max_entropy(glmnet_params, size = 20)
glmnet_grid
#> # A tibble: 20 x 2
#>     penalty mixture
#>       <dbl>   <dbl>
#>  1 2.94e- 1  0.702 
#>  2 1.48e- 4  0.996 
#>  3 1.60e- 1  0.444 
#>  4 5.86e- 1  0.975 
#>  5 1.69e- 9  0.0491
#>  6 1.10e- 5  0.699 
#>  7 2.76e- 2  0.988 
#>  8 4.95e- 8  0.753 
#>  9 1.07e- 5  0.382 
#> 10 7.87e- 8  0.331 
#> 11 4.07e- 1  0.180 
#> 12 1.70e- 3  0.590 
#> 13 2.52e-10  0.382 
#> 14 2.47e-10  0.666 
#> 15 2.31e- 9  0.921 
#> 16 1.31e- 7  0.546 
#> 17 1.49e- 6  0.973 
#> 18 1.28e- 3  0.0224
#> 19 7.49e- 7  0.0747
#> 20 2.37e- 3  0.351


### Creating a workflow for each model
glmnet_wfl <- workflow() %>%
       add_recipe(recipe) %>%
       add_model(glmnet_model)

### Creating cross-validation splits
cv_splits <- vfold_cv(train, v = 5)
cv_splits
#> #  5-fold cross-validation 
#> # A tibble: 5 x 2
#>   splits         id   
#>   <list>         <chr>
#> 1 <split [20/6]> Fold1
#> 2 <split [21/5]> Fold2
#> 3 <split [21/5]> Fold3
#> 4 <split [21/5]> Fold4
#> 5 <split [21/5]> Fold5


### Training and tuning models
#### GLMNET
glmnet_stage_1_cv_results_tbl <- tune_grid(
       glmnet_wfl,
       resamples = cv_splits,
       grid = glmnet_grid,
       metrics = metric_set(mae, mape, rmse, rsq),
       control = control_grid(verbose = TRUE)
)
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glmnet_stage_1_cv_results_tbl %>% show_best("mae", n = 10)
#> # A tibble: 10 x 8
#>     penalty mixture .metric .estimator  mean     n std_err .config
#>       <dbl>   <dbl> <chr>   <chr>      <dbl> <int>   <dbl> <chr>  
#>  1 4.95e- 8  0.753  mae     standard    19.1     5    1.56 Model15
#>  2 1.49e- 6  0.973  mae     standard    19.1     5    1.56 Model17
#>  3 2.31e- 9  0.921  mae     standard    19.1     5    1.56 Model16
#>  4 1.10e- 5  0.699  mae     standard    19.1     5    1.56 Model13
#>  5 1.31e- 7  0.546  mae     standard    19.1     5    1.56 Model10
#>  6 2.47e-10  0.666  mae     standard    19.1     5    1.56 Model12
#>  7 7.87e- 8  0.331  mae     standard    19.1     5    1.56 Model05
#>  8 1.07e- 5  0.382  mae     standard    19.1     5    1.56 Model08
#>  9 2.52e-10  0.382  mae     standard    19.1     5    1.56 Model07
#> 10 7.49e- 7  0.0747 mae     standard    19.1     5    1.56 Model03

由reprex 包（v0.3.0）于 2020 年 9 月 4 日创建

4

1 回答 1

4

在配方中转换结果可能非常棘手。假设您确实需要执行以下操作：

step_log(mpg, base = 10)

在结果未知的情况下将配方应用于新数据时，这将导致失败。由于燃油效率是我们试图预测的，因此该变量的数据中可能不会有一列。事实上，为了避免信息泄露，在调整期间，tune_grid()用于训练每个模型的数据与用于评估每个模型的数据是隔离的。这意味着训练集和任何结果列在预测时都不可使用。

对于结果列的简单转换，我们强烈建议在配方之外进行这些操作。

library(tidymodels)

log_car <- mtcars %>%
  mutate(mpg = log10(mpg))

set.seed(101)
clean_split <- initial_split(log_car, prop = 0.8)

train <- training(clean_split)
test <- testing(clean_split)


recipe <- recipe(mpg ~ ., data = train) %>%
  step_other(all_nominal(), threshold = 0.01) %>%
  step_dummy(all_nominal()) %>% 
  step_normalize(all_predictors(), -all_nominal()) %>%
  step_zv(all_predictors(), -all_outcomes())

glmnet_model <- linear_reg(mode = "regression",
                           penalty = tune(),
                           mixture = tune()) %>%
  set_engine("glmnet")

glmnet_model
#> Linear Regression Model Specification (regression)
#> 
#> Main Arguments:
#>   penalty = tune()
#>   mixture = tune()
#> 
#> Computational engine: glmnet

set.seed(123)
glmnet_grid <- grid_max_entropy(penalty(), mixture(), size = 20)
glmnet_grid
#> # A tibble: 20 x 2
#>     penalty mixture
#>       <dbl>   <dbl>
#>  1 2.94e- 1  0.702 
#>  2 1.48e- 4  0.996 
#>  3 1.60e- 1  0.444 
#>  4 5.86e- 1  0.975 
#>  5 1.69e- 9  0.0491
#>  6 1.10e- 5  0.699 
#>  7 2.76e- 2  0.988 
#>  8 4.95e- 8  0.753 
#>  9 1.07e- 5  0.382 
#> 10 7.87e- 8  0.331 
#> 11 4.07e- 1  0.180 
#> 12 1.70e- 3  0.590 
#> 13 2.52e-10  0.382 
#> 14 2.47e-10  0.666 
#> 15 2.31e- 9  0.921 
#> 16 1.31e- 7  0.546 
#> 17 1.49e- 6  0.973 
#> 18 1.28e- 3  0.0224
#> 19 7.49e- 7  0.0747
#> 20 2.37e- 3  0.351

glmnet_wfl <- workflow() %>%
  add_recipe(recipe) %>%
  add_model(glmnet_model)


cv_splits <- vfold_cv(train, v = 5)
cv_splits
#> #  5-fold cross-validation 
#> # A tibble: 5 x 2
#>   splits         id   
#>   <list>         <chr>
#> 1 <split [20/6]> Fold1
#> 2 <split [21/5]> Fold2
#> 3 <split [21/5]> Fold3
#> 4 <split [21/5]> Fold4
#> 5 <split [21/5]> Fold5

glmnet_stage_1_cv_results_tbl <- tune_grid(
  glmnet_wfl,
  resamples = cv_splits,
  grid = glmnet_grid,
  metrics = metric_set(mae)
)
#> Loading required package: Matrix
#> 
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#> 
#>     expand, pack, unpack
#> Loaded glmnet 4.0-2

glmnet_stage_1_cv_results_tbl %>% 
  show_best("mae", n = 10)
#> # A tibble: 10 x 8
#>     penalty mixture .metric .estimator   mean     n std_err .config
#>       <dbl>   <dbl> <chr>   <chr>       <dbl> <int>   <dbl> <chr>  
#>  1 1.70e- 3  0.590  mae     standard   0.0396     5 0.00583 Model11
#>  2 2.37e- 3  0.351  mae     standard   0.0400     5 0.00597 Model06
#>  3 1.28e- 3  0.0224 mae     standard   0.0433     5 0.00592 Model01
#>  4 1.48e- 4  0.996  mae     standard   0.0462     5 0.00538 Model20
#>  5 2.76e- 2  0.988  mae     standard   0.0465     5 0.00699 Model19
#>  6 1.69e- 9  0.0491 mae     standard   0.0470     5 0.00536 Model02
#>  7 7.49e- 7  0.0747 mae     standard   0.0474     5 0.00539 Model03
#>  8 2.47e-10  0.666  mae     standard   0.0476     5 0.00545 Model12
#>  9 1.10e- 5  0.699  mae     standard   0.0476     5 0.00546 Model13
#> 10 2.31e- 9  0.921  mae     standard   0.0476     5 0.00547 Model16

由reprex 包于 2020-09-06 创建(v0.3.0.9001)

于 2020-09-06T23:40:23.660 回答