Примените confusionMatrix с помощью оператора конвейера в R - PullRequest
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Примените confusionMatrix с помощью оператора конвейера в R

0 голосов
/ 26 мая 2020

Я хочу применить confusionMatrix к объекту xtabs с помощью оператора вертикальной черты. Я использовал следующий код 

library(tidyverse)
df %>% 
  xtabs( ~ Observed + Forecasted + Station, data =.) %>% 
  caret::confusionMatrix(.)

Это дает мне следующую ошибку:

Ошибка в confusionMatrix.table (.): Таблица должна иметь два измерения

Я мог бы применить его для отдельных станций, например 

df %>% subset(Station == "Aizawl") %>% 
  xtabs( ~ Observed + Forecasted, data =.) %>% 
  caret::confusionMatrix(.)

Теперь, как вычислить confusionMatrix для всех станций одновременно?

Данные 

df = structure(list(Station = c("Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", "Aizawl", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", 
"Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip", "Serchhip"
), Observed = c(1, 1, 1, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 
1, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 3, 3, 4, 1, 1, 4, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
4, 4, 4, 3, 4, 1, 1, 1, 1, 1, 3, 5, 5, 5, 3, 1, 1, 3, 1, 1, 1, 
1, 1, 5, 3, 4, 1, 1, 1, 1, 1, 3, 1, 4, 1, 1, 1, 1, 1, 4, 4, 5, 
1, 5, 4, 5, 5, 5, 5, 1, 5, 1, 4, 5, 4, 4, 5, 4, 5, 5, 3, 1, 5, 
3, 4, 3, 4, 5, 5, 5, 5, 4, 4, 5, 4, 4, 5, 5, 5, 5, 4, 5, 5, 5, 
5, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 3, 5, 5, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 
3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 3, 
3, 3, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 3, 4, 1, 1, 1, 1, 1, 1, 1, 
1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 3, 
6, 5, 5, 4, 1, 5, 1, 1, 1, 1, 4, 5, 5, 5, 5, 5, 5, 1, 1, 4, 1, 
4, 4, 4, 5, 1, 1, 4, 3, 5, 4, 5, 5, 5, 5, 5, 4, 4, 4, 4, 5, 1, 
6, 5, 5), Forecasted = c(1, 1, 1, 5, 5, 1, 1, 1, 5, 5, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 5, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 4, 4, 1, 1, 5, 3, 1, 
1, 1, 4, 5, 5, 5, 5, 1, 1, 1, 5, 5, 1, 5, 5, 5, 4, 5, 4, 4, 4, 
3, 4, 4, 1, 1, 5, 5, 4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 4, 1, 1, 
5, 4, 4, 5, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 5, 4, 5, 5, 4, 1, 1, 
4, 4, 5, 5, 5, 5, 1, 4, 5, 5, 1, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 1, 1, 1, 5, 4, 1, 1, 1, 5, 4, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 6, 5, 5, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 5, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 5, 5, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 4, 4, 4, 1, 4, 1, 3, 
1, 1, 1, 4, 4, 4, 4, 4, 4, 1, 1, 1, 4, 4, 3, 5, 5, 5, 4, 3, 5, 
5, 5, 5, 5, 4, 5, 5, 5, 4, 5, 4, 4, 5, 5, 4, 4, 5, 4, 1, 4, 4, 
5, 5, 4, 5, 4, 5, 4, 5, 5, 5, 1, 4, 5, 5, 5, 4, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 6)), row.names = c(NA, 333L), class = "data.frame")

1 Ответ

1 голос
/ 28 мая 2020

Если вы сделаете это для всего data.frame, вы получите массив таблиц, разделенных станциями, поэтому confusionMatrix () жалуется: 

xtabs( ~ Observed + Forecasted + Station, data =df)
, , Station = Aizawl

        Forecasted
Observed  1  3  4  5  6
       1 56  2 13 13  0
       3 12  0  4  8  0
       4  4  0  9 11  0
       5  3  0  7 23  0
       6  0  0  0  0  0

, , Station = Serchhip

        Forecasted
Observed  1  3  4  5  6
       1 76  3 18 18  0
       3  4  0  2  5  0
       4  2  0  4 12  0
       5  1  0  9 10  2
       6  0  0  0  2  0

Итак, вы можете попробовать использовать array_tree() с margin = 3 чтобы преобразовать его в список, с которым вы можете использовать map () для применения матрицы путаницы: 

df %>% 
xtabs( ~ Observed + Forecasted + Station, data =.) %>% 
array_tree(.,margin=3) %>% 
map(~caret::confusionMatrix(as.table(.x)))
...