Score a segmentation against labels

Counts label errors: a positive region with no changepoint is a false negative, a negative region with one is a false positive, and a "one_change" region with two or more is a false positive as well. This is the accuracy measure supervised changepoint detection is built on, and, unlike an information criterion, it is defined by what the expert asserted rather than by a model assumption.

Usage

cpt_label_error(object, labels)

# S3 method for class 'cpt_label_error'
tidy(x, ...)

# S3 method for class 'cpt_label_error'
print(x, ...)

Arguments

object

A ggcpt object, or an integer vector of changepoint positions.

labels

A cpt_labels tibble (or anything with start/end/change columns).

x

A cpt_label_error object (for print()).

...

Ignored.

Value

A tibble with one row per label: label_id, series (the label set's series identifier, NA for a single unnamed series), start, end, change, n_changes (how many detections fell inside), status ("correct", "false_positive" or "false_negative"), carrying the totals in an errors attribute and printing them. An empty label set gives a zero-row tibble with the same columns.

See also

cpt_labels(), cpt_label_error_curve(), geom_cpt_label().

Examples

set.seed(2026)
fit <- cpt_detect(c(rnorm(50), rnorm(50, 4)), method = "pelt")
labs <- cpt_labels(c(40, 70), c(60, 95), c("one_change", "no_change"))
cpt_label_error(fit, labs)
#> cpt_label_error (2 label(s))
#>   correct: 2   false positives: 0   false negatives: 0
#>   total label errors: 0
#> 
#> # A tibble: 2 × 7
#>   label_id series start   end change     n_changes status 
#>      <int> <chr>  <int> <int> <chr>          <int> <chr>  
#> 1        1 NA        40    60 one_change         1 correct
#> 2        2 NA        70    95 no_change          0 correct