GLMNET 输出产生平坦的 MAE
GLMNET output produces flat MAE
我想知道我在下面 reprex 的 recipe 中做错了什么。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，所以也许我在这里遗漏了一个学习技巧，但请联系我看看是否有经验丰富的人可以帮助指导我。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 package (v0.3.0)
于 2020-09-04 创建
在食谱中改变结果可能非常棘手。假设您确实需要执行以下操作：
step_log(mpg, base = 10)
当配方应用于新数据且结果未知时，这将导致失败。由于燃油效率是我们试图预测的，因此数据中可能不会有此变量的列。事实上，为了避免 信息泄漏 ，在使用 tune_grid() 进行调整期间，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不可使用。
对于结果列的简单转换，我们强烈建议这些操作在配方之外进行。
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 package (v0.3.0.9001)
于 2020-09-06 创建
我想知道我在下面 reprex 的 recipe 中做错了什么。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，所以也许我在这里遗漏了一个学习技巧，但请联系我看看是否有经验丰富的人可以帮助指导我。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 package (v0.3.0)
于 2020-09-04 创建在食谱中改变结果可能非常棘手。假设您确实需要执行以下操作：
step_log(mpg, base = 10)
当配方应用于新数据且结果未知时，这将导致失败。由于燃油效率是我们试图预测的，因此数据中可能不会有此变量的列。事实上，为了避免 信息泄漏 ，在使用 tune_grid() 进行调整期间，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不可使用。
对于结果列的简单转换，我们强烈建议这些操作在配方之外进行。
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 package (v0.3.0.9001)
于 2020-09-06 创建