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R yardstick metric_set 组合度量函数


metric_set() 允许您将多个度量函数组合在一起形成一个新函数，该函数可以一次计算所有这些函数。

用法

metric_set(...)

参数

...

要包含在指标集中的函数的裸名称。

细节

所有函数必须是：

  • 仅数字指标

  • 类指标或类概率指标的混合

  • 动态、综合和静态生存指标的组合

例如，rmse() 可以与 mae() 一起使用，因为它们是数字指标，但不能与 accuracy() 一起使用，因为它是分类指标。但 accuracy() 可以与 roc_auc() 一起使用。

返回的度量函数将具有不同的参数列表，具体取决于传入的是数字度量还是类/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
)

混合类和类概率指标时，将硬预测(因子列)作为命名参数 estimate 传递，将软预测(类概率列)作为裸列名称或 tidyselect 选择器传递到 ... 。

混合动态、集成和静态生存指标时，将时间预测作为命名参数 estimate 传递，并将生存预测作为裸列名称或 tidyselect 选择器传递给 ... 。

如果 metric_tweak() 已用于 "tweak" 这些参数之一，例如 estimator 或 event_level ，则调整后的版本获胜。这允许您逐个度量地设置估计器，并仍然在 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
源代码：R/metrics.R

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注：本文由纯净天空筛选整理自Max Kuhn等大神的英文原创作品 Combine metric functions。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。