如何提取由Tidymodels生成的GLMNET系数

Question

如何提取由Tidymodels生成的GLMNET系数

4

我使用tidymodels估计了一个glmnet逻辑回归。但是在tidymodels中，我无法弄清楚两件密切相关的事情：

a) 如何提取估计系数
b) 将估计模型保存以备将来生产使用。

以下是伪模型的代码。我尝试了tidy()、coef()和predict()，但它们都失败了。任何帮助将不胜感激。谢谢。

library(tidymodels)
#> -- Attaching packages --------------------------------------------------------------------------------------------------------------------------- tidymodels 0.1.0 --
#> v broom     0.7.0      v recipes   0.1.13
#> v dials     0.0.8      v rsample   0.0.7 
#> v dplyr     1.0.0      v tibble    3.0.3 
#> v ggplot2   3.3.2      v tune      0.1.1 
#> v infer     0.5.2      v workflows 0.1.2 
#> v parsnip   0.1.2      v yardstick 0.0.7 
#> v 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()

set.seed(1234)

train <- tibble(y = factor(sample(c(0,1), 1000, replace = TRUE)),
                x1 = rnorm(1000),
                x2 = rnorm(1000)
                )

test <- tibble(y = factor(sample(c(0,1), 300, replace = TRUE)),
               x1 = rnorm(300),
               x2 = rnorm(300)
               )

lr_cv <- vfold_cv(train)

lr_mod <- logistic_reg(penalty = tune(), mixture = 1) %>%
  set_mode("classification") %>%
  set_engine("glmnet")

lr_wf <- workflow() %>%
  add_model(lr_mod) %>%
  add_formula(y ~ .)

lr_grid <- tibble(penalty = 10^seq(-4,1, length.out = 5))

lr_tune <- tune_grid(lr_wf, resamples = lr_cv, grid = lr_grid, metrics = metric_set(roc_auc))

lr_best <- select_best(lr_tune)
lr_best
#> # A tibble: 1 x 2
#>   penalty .config
#>     <dbl> <chr>  
#> 1  0.0001 Model1

lr_wf_final <- lr_wf %>% finalize_workflow(lr_best)

final_mod <- lr_wf_final %>% fit(train)

# the followings were tried but didn't work
lambda <- lr_best[1]
lambda
#> # A tibble: 1 x 1
#>   penalty
#>     <dbl>
#> 1  0.0001

predict(final.mod , s = lambda, type ="coefficients")[1:3 ,]
#> Error in predict(final.mod, s = lambda, type = "coefficients"): object 'final.mod' not found

tidy(final_mod)
#> # A tibble: 35 x 5
#>    term         step estimate  lambda dev.ratio
#>    <chr>       <dbl>    <dbl>   <dbl>     <dbl>
#>  1 (Intercept)     1  -0.0560 0.0183   1.71e-14
#>  2 (Intercept)     2  -0.0561 0.0167   1.65e- 4
#>  3 (Intercept)     3  -0.0562 0.0152   3.02e- 4
#>  4 (Intercept)     4  -0.0562 0.0139   4.16e- 4
#>  5 (Intercept)     5  -0.0563 0.0126   5.10e- 4
#>  6 (Intercept)     6  -0.0564 0.0115   5.89e- 4
#>  7 (Intercept)     7  -0.0564 0.0105   6.54e- 4
#>  8 (Intercept)     8  -0.0565 0.00956  7.08e- 4
#>  9 (Intercept)     9  -0.0565 0.00871  7.53e- 4
#> 10 (Intercept)    10  -0.0566 0.00794  7.90e- 4
#> # ... with 25 more rows

coef(final_mod)
#> NULL

^{本文创建于2020年07月25日，使用reprex软件包 (v0.3.0)}

- nyk

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- Julia Silge · Accepted Answer

我们最近为获取预估系数添加了一些更新的支持。

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     1.0.0          ✓ tibble    3.0.3     
#> ✓ ggplot2   3.3.2          ✓ tidyr     1.1.0     
#> ✓ infer     0.5.3          ✓ tune      0.1.1.9000
#> ✓ modeldata 0.0.2          ✓ workflows 0.1.2     
#> ✓ parsnip   0.1.2.9000     ✓ 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()

set.seed(1234)
train <- tibble(y = factor(sample(c(0,1), 1000, replace = TRUE)),
                x1 = as.numeric(y) + rnorm(1000),
                x2 = rnorm(1000),
                x3 = rnorm(1000)
)

lr_cv <- vfold_cv(train)
lr_grid <- tibble(penalty = 10^seq(-4,1, length.out = 5))
lr_mod <- logistic_reg(penalty = tune(), mixture = 1) %>%
  set_mode("classification") %>%
  set_engine("glmnet")

lr_wf <- workflow() %>%
  add_model(lr_mod) %>%
  add_formula(y ~ .) 

lr_best <- lr_wf %>%
  tune_grid(
    resamples = lr_cv,
    grid = lr_grid
  ) %>%
  select_best("roc_auc")

lr_wf %>% 
  finalize_workflow(lr_best) %>%
  fit(train) %>%
  pull_workflow_fit() %>%
  tidy()
#> Loading required package: Matrix
#> 
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#> 
#>     expand, pack, unpack
#> Loaded glmnet 4.0-2
#> # A tibble: 4 x 3
#>   term        estimate penalty
#>   <chr>          <dbl>   <dbl>
#> 1 (Intercept)   -1.30   0.0316
#> 2 x1             0.834  0.0316
#> 3 x2             0      0.0316
#> 4 x3             0      0.0316

^{本文创建于2020-07-30，使用reprex package (v0.3.0.9001)。}

提取系数的步骤是获取已拟合的对象，然后进行整理。

如果您想将此对象保存供以后使用，可以选择仅保存这些系数（毕竟它是一个线性模型），或者将最终完成并已拟合的工作流保存为二进制对象，并可进行预测。