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R yardstick metric_set 組合度量函數


metric_set() 允許您將多個度量函數組合在一起形成一個新函數，該函數可以一次計算所有這些函數。

用法

metric_set(...)

參數

...

要包含在指標集中的函數的裸名稱。

細節

所有函數必須是：

  • 僅數字指標

  • 類指標或類概率指標的混合

  • 動態、綜合和靜態生存指標的組合

例如，rmse() 可以與 mae() 一起使用，因為它們是數字指標，但不能與 accuracy() 一起使用，因為它是分類指標。但 accuracy() 可以與 roc_auc() 一起使用。

返回的度量函數將具有不同的參數列表，具體取決於傳入的是數字度量還是類/概率度量的混合。

# Numeric metric set signature:
fn(
  data,
  truth,
  estimate,
  na_rm = TRUE,
  case_weights = NULL,
  ...
)

# Class / prob metric set signature:
fn(
  data,
  truth,
  ...,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  event_level = yardstick_event_level(),
  case_weights = NULL
)

# Dynamic / integrated / static survival metric set signature:
fn(
  data,
  truth,
  ...,
  estimate,
  na_rm = TRUE,
  case_weights = NULL
)

混合類和類概率指標時，將硬預測(因子列)作為命名參數 estimate 傳遞，將軟預測(類概率列)作為裸列名稱或 tidyselect 選擇器傳遞到 ... 。

混合動態、集成和靜態生存指標時，將時間預測作為命名參數 estimate 傳遞，並將生存預測作為裸列名稱或 tidyselect 選擇器傳遞給 ... 。

如果 metric_tweak() 已用於 "tweak" 這些參數之一，例如 estimator 或 event_level ，則調整後的版本獲勝。這允許您逐個度量地設置估計器，並仍然在 metric_set() 中使用它。

也可以看看

例子

library(dplyr)

# Multiple regression metrics
multi_metric <- metric_set(rmse, rsq, ccc)

# The returned function has arguments:
# fn(data, truth, estimate, na_rm = TRUE, ...)
multi_metric(solubility_test, truth = solubility, estimate = prediction)
#> # A tibble: 3 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 rmse    standard       0.722
#> 2 rsq     standard       0.879
#> 3 ccc     standard       0.937

# Groups are respected on the new metric function
class_metrics <- metric_set(accuracy, kap)

hpc_cv %>%
  group_by(Resample) %>%
  class_metrics(obs, estimate = pred)
#> # A tibble: 20 × 4
#>    Resample .metric  .estimator .estimate
#>    <chr>    <chr>    <chr>          <dbl>
#>  1 Fold01   accuracy multiclass     0.726
#>  2 Fold02   accuracy multiclass     0.712
#>  3 Fold03   accuracy multiclass     0.758
#>  4 Fold04   accuracy multiclass     0.712
#>  5 Fold05   accuracy multiclass     0.712
#>  6 Fold06   accuracy multiclass     0.697
#>  7 Fold07   accuracy multiclass     0.675
#>  8 Fold08   accuracy multiclass     0.721
#>  9 Fold09   accuracy multiclass     0.673
#> 10 Fold10   accuracy multiclass     0.699
#> 11 Fold01   kap      multiclass     0.533
#> 12 Fold02   kap      multiclass     0.512
#> 13 Fold03   kap      multiclass     0.594
#> 14 Fold04   kap      multiclass     0.511
#> 15 Fold05   kap      multiclass     0.514
#> 16 Fold06   kap      multiclass     0.486
#> 17 Fold07   kap      multiclass     0.454
#> 18 Fold08   kap      multiclass     0.531
#> 19 Fold09   kap      multiclass     0.454
#> 20 Fold10   kap      multiclass     0.492

# ---------------------------------------------------------------------------

# If you need to set options for certain metrics,
# do so by wrapping the metric and setting the options inside the wrapper,
# passing along truth and estimate as quoted arguments.
# Then add on the function class of the underlying wrapped function,
# and the direction of optimization.
ccc_with_bias <- function(data, truth, estimate, na_rm = TRUE, ...) {
  ccc(
    data = data,
    truth = !!rlang::enquo(truth),
    estimate = !!rlang::enquo(estimate),
    # set bias = TRUE
    bias = TRUE,
    na_rm = na_rm,
    ...
  )
}

# Use `new_numeric_metric()` to formalize this new metric function
ccc_with_bias <- new_numeric_metric(ccc_with_bias, "maximize")

multi_metric2 <- metric_set(rmse, rsq, ccc_with_bias)

multi_metric2(solubility_test, truth = solubility, estimate = prediction)
#> # A tibble: 3 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 rmse    standard       0.722
#> 2 rsq     standard       0.879
#> 3 ccc     standard       0.937

# ---------------------------------------------------------------------------
# A class probability example:

# Note that, when given class or class prob functions,
# metric_set() returns a function with signature:
# fn(data, truth, ..., estimate)
# to be able to mix class and class prob metrics.

# You must provide the `estimate` column by explicitly naming
# the argument

class_and_probs_metrics <- metric_set(roc_auc, pr_auc, accuracy)

hpc_cv %>%
  group_by(Resample) %>%
  class_and_probs_metrics(obs, VF:L, estimate = pred)
#> # A tibble: 30 × 4
#>    Resample .metric  .estimator .estimate
#>    <chr>    <chr>    <chr>          <dbl>
#>  1 Fold01   accuracy multiclass     0.726
#>  2 Fold02   accuracy multiclass     0.712
#>  3 Fold03   accuracy multiclass     0.758
#>  4 Fold04   accuracy multiclass     0.712
#>  5 Fold05   accuracy multiclass     0.712
#>  6 Fold06   accuracy multiclass     0.697
#>  7 Fold07   accuracy multiclass     0.675
#>  8 Fold08   accuracy multiclass     0.721
#>  9 Fold09   accuracy multiclass     0.673
#> 10 Fold10   accuracy multiclass     0.699
#> # ℹ 20 more rows
源代碼：R/metrics.R

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注：本文由純淨天空篩選整理自Max Kuhn等大神的英文原創作品 Combine metric functions。非經特殊聲明，原始代碼版權歸原作者所有，本譯文未經允許或授權，請勿轉載或複製。