yardstick ROC

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#1. 安装

> install.packages("yardstick")

#2. 调用包

> library(yardstick)
> library(dplyr)
> library(ggplot2)

#3. ROC 分析

##3.1 示例数据

> data("hpc_cv")
> hpc_cv <- as_tibble(hpc_cv)
> hpc_cv
#> # A tibble: 3,467 x 7
#>    obs   pred     VF      F       M          L Resample
#>    <fct> <fct> <dbl>  <dbl>   <dbl>      <dbl> <chr>   
#>  1 VF    VF    0.914 0.0779 0.00848 0.0000199  Fold01  
#>  2 VF    VF    0.938 0.0571 0.00482 0.0000101  Fold01  
#>  3 VF    VF    0.947 0.0495 0.00316 0.00000500 Fold01  
#>  4 VF    VF    0.929 0.0653 0.00579 0.0000156  Fold01  
#>  5 VF    VF    0.942 0.0543 0.00381 0.00000729 Fold01  
#>  6 VF    VF    0.951 0.0462 0.00272 0.00000384 Fold01  
#>  7 VF    VF    0.914 0.0782 0.00767 0.0000354  Fold01  
#>  8 VF    VF    0.918 0.0744 0.00726 0.0000157  Fold01  
#>  9 VF    VF    0.843 0.128  0.0296  0.000192   Fold01  
#> 10 VF    VF    0.920 0.0728 0.00703 0.0000147  Fold01  
#> # … with 3,457 more rows

##3.2 roc_auc()计算 ROC AUC

hpc_cv %>%
  group_by(Resample) %>%
  roc_auc(obs, VF:L)
#> # A tibble: 10 x 4
#>    Resample .metric .estimator .estimate
#>    <chr>    <chr>   <chr>          <dbl>
#>  1 Fold01   roc_auc hand_till      0.831
#>  2 Fold02   roc_auc hand_till      0.817
#>  3 Fold03   roc_auc hand_till      0.869
#>  4 Fold04   roc_auc hand_till      0.849
#>  5 Fold05   roc_auc hand_till      0.811
#>  6 Fold06   roc_auc hand_till      0.836
#>  7 Fold07   roc_auc hand_till      0.825
#>  8 Fold08   roc_auc hand_till      0.846
#>  9 Fold09   roc_auc hand_till      0.836
#> 10 Fold10   roc_auc hand_till      0.820

##3.3 roc_curve() 获取ROC曲线

hpc_cv %>%
  group_by(Resample) %>%
  roc_curve(obs, VF:L) %>%
  autoplot()

ROC curve.png

#4. precision recall curve

##4.1 示例数据

> head(two_class_example)
   truth      Class1       Class2 predicted
1 Class2 0.003589243 0.9964107574    Class2
2 Class1 0.678621054 0.3213789460    Class1
3 Class2 0.110893522 0.8891064779    Class2
4 Class1 0.735161703 0.2648382969    Class1
5 Class2 0.016239960 0.9837600397    Class2
6 Class1 0.999275071 0.0007249286    Class1

##4.2 pr_curve() 计算precision recall curve

• 结果以tibble形式返回

# Two class - a tibble is returned
pr_curve(two_class_example, truth, Class1)
#> # A tibble: 501 x 3
#>    .threshold  recall precision
#>         <dbl>   <dbl>     <dbl>
#>  1    Inf     0              NA
#>  2      1.000 0.00388         1
#>  3      1.000 0.00775         1
#>  4      1.000 0.0116          1
#>  5      1.000 0.0155          1
#>  6      1.000 0.0194          1
#>  7      1.000 0.0233          1
#>  8      1.000 0.0271          1
#>  9      1.000 0.0310          1
#> 10      1.000 0.0349          1
#> # … with 491 more rows

##4.3 使用ggplot2 可视化precision recall curve

pr_curve(two_class_example, truth, Class1) %>%
  ggplot(aes(x = recall, y = precision)) +
  geom_path() +
  coord_equal() +
  theme_bw()

precision recall curve

##4.4 使用autoplot()可视化precision recall curve

> autoplot(pr_curve(two_class_example, truth, Class1))

precision recall curve

##4.5 多个水平的precision recall curve

• obs下每个水平的precision recall curve

#obs 下多个水平
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  count(obs)
# A tibble: 4 x 2
  obs       n
  <fct> <int>
1 VF      177
2 F       108
3 M        41
4 L        21

#作图
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  pr_curve(obs, VF:L) %>%
  autoplot()

One curve per level

• 依据Resample下各个水平分组，obs下每个水平precision recall curve

hpc_cv %>%
  group_by(Resample) %>%
  pr_curve(obs, VF:L) %>%
  autoplot()

One curve per level group_by.png

#5 参考

yardstick package | R Documentation