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pr_auc() is a metric that computes the area under the precision recall curve. See pr_curve() for the full curve.

Usage

pr_auc(data, ...)

# S3 method for data.frame
pr_auc(
  data,
  truth,
  ...,
  estimator = NULL,
  na_rm = TRUE,
  event_level = yardstick_event_level(),
  case_weights = NULL
)

pr_auc_vec(
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  event_level = yardstick_event_level(),
  case_weights = NULL,
  ...
)

Arguments

data

A data.frame containing the columns specified by truth and ....

...

A set of unquoted column names or one or more dplyr selector functions to choose which variables contain the class probabilities. If truth is binary, only 1 column should be selected. Otherwise, there should be as many columns as factor levels of truth.

truth

The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a factor vector.

estimator

One of "binary", "macro", or "macro_weighted" to specify the type of averaging to be done. "binary" is only relevant for the two class case. The other two are general methods for calculating multiclass metrics. The default will automatically choose "binary" or "macro" based on truth.

na_rm

A logical value indicating whether NA values should be stripped before the computation proceeds.

event_level

A single string. Either "first" or "second" to specify which level of truth to consider as the "event". This argument is only applicable when estimator = "binary". The default uses an internal helper that generally defaults to "first", however, if the deprecated global option yardstick.event_first is set, that will be used instead with a warning.

case_weights

The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector.

estimate

If truth is binary, a numeric vector of class probabilities corresponding to the "relevant" class. Otherwise, a matrix with as many columns as factor levels of truth. It is assumed that these are in the same order as the levels of truth.

Value

A tibble with columns .metric, .estimator, and .estimate and 1 row of values. For grouped data frames, the number of rows returned will be the same as the number of groups. For pr_auc_vec(), a single numeric value (or NA).

Multiclass

Macro and macro-weighted averaging is available for this metric. The default is to select macro averaging if a truth factor with more than 2 levels is provided. Otherwise, a standard binary calculation is done. See vignette("multiclass", "yardstick") for more information.

Relevant Level

There is no common convention on which factor level should automatically be considered the "event" or "positive" result when computing binary classification metrics. In yardstick, the default is to use the first level. To alter this, change the argument event_level to "second" to consider the last level of the factor the level of interest. For multiclass extensions involving one-vs-all comparisons (such as macro averaging), this option is ignored and the "one" level is always the relevant result.

See also

pr_curve() for computing the full precision recall curve.

Other class probability metrics: average_precision(), classification_cost(), gain_capture(), mn_log_loss(), roc_auc(), roc_aunp(), roc_aunu()

Author

Max Kuhn

Examples

# ---------------------------------------------------------------------------
# Two class example

# `truth` is a 2 level factor. The first level is `"Class1"`, which is the
# "event of interest" by default in yardstick. See the Relevant Level
# section above.
data(two_class_example)

# Binary metrics using class probabilities take a factor `truth` column,
# and a single class probability column containing the probabilities of
# the event of interest. Here, since `"Class1"` is the first level of
# `"truth"`, it is the event of interest and we pass in probabilities for it.
pr_auc(two_class_example, truth, Class1)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 pr_auc  binary         0.946

# ---------------------------------------------------------------------------
# Multiclass example

# `obs` is a 4 level factor. The first level is `"VF"`, which is the
# "event of interest" by default in yardstick. See the Relevant Level
# section above.
data(hpc_cv)

# You can use the col1:colN tidyselect syntax
library(dplyr)
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  pr_auc(obs, VF:L)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 pr_auc  macro          0.611

# Change the first level of `obs` from `"VF"` to `"M"` to alter the
# event of interest. The class probability columns should be supplied
# in the same order as the levels.
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  mutate(obs = relevel(obs, "M")) %>%
  pr_auc(obs, M, VF:L)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 pr_auc  macro          0.611

# Groups are respected
hpc_cv %>%
  group_by(Resample) %>%
  pr_auc(obs, VF:L)
#> # A tibble: 10 × 4
#>    Resample .metric .estimator .estimate
#>    <chr>    <chr>   <chr>          <dbl>
#>  1 Fold01   pr_auc  macro          0.611
#>  2 Fold02   pr_auc  macro          0.620
#>  3 Fold03   pr_auc  macro          0.689
#>  4 Fold04   pr_auc  macro          0.680
#>  5 Fold05   pr_auc  macro          0.620
#>  6 Fold06   pr_auc  macro          0.650
#>  7 Fold07   pr_auc  macro          0.607
#>  8 Fold08   pr_auc  macro          0.650
#>  9 Fold09   pr_auc  macro          0.628
#> 10 Fold10   pr_auc  macro          0.603

# Weighted macro averaging
hpc_cv %>%
  group_by(Resample) %>%
  pr_auc(obs, VF:L, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#>    Resample .metric .estimator     .estimate
#>    <chr>    <chr>   <chr>              <dbl>
#>  1 Fold01   pr_auc  macro_weighted     0.746
#>  2 Fold02   pr_auc  macro_weighted     0.743
#>  3 Fold03   pr_auc  macro_weighted     0.789
#>  4 Fold04   pr_auc  macro_weighted     0.754
#>  5 Fold05   pr_auc  macro_weighted     0.737
#>  6 Fold06   pr_auc  macro_weighted     0.743
#>  7 Fold07   pr_auc  macro_weighted     0.748
#>  8 Fold08   pr_auc  macro_weighted     0.756
#>  9 Fold09   pr_auc  macro_weighted     0.711
#> 10 Fold10   pr_auc  macro_weighted     0.737

# Vector version
# Supply a matrix of class probabilities
fold1 <- hpc_cv %>%
  filter(Resample == "Fold01")

pr_auc_vec(
   truth = fold1$obs,
   matrix(
     c(fold1$VF, fold1$F, fold1$M, fold1$L),
     ncol = 4
   )
)
#> [1] 0.6109931