Computes standard accuracy metrics comparing predicted changepoints to ground truth, including precision/recall/F1 with margin, covering metric, Hausdorff distance, adjusted Rand index, annotation error, and MAE/RMSE of matched locations.
Value
A tibble with columns: n, n_pred, n_truth,
precision, recall, f1, covering,
hausdorff, rand_index, annotation_error,
mae_matched, rmse_matched.
Details
Precision/recall use a one-to-one matching: each truth may be
claimed by at most one prediction (predictions are scanned in order and
take the earliest unmatched truth within margin, which yields a
maximum matching for interval-structured problems). When pred and
truth are both empty the segmentation is exactly right, so
precision, recall, and F1 are all 1. The covering metric follows
van den Burg and Williams (2020): the prediction-side partition is always
well defined, so an empty pred scores the covering of the trivial
single-segment partition rather than 0.
Examples
cpt_metrics(c(100, 200), c(100, 200), n = 300)
#> # A tibble: 1 × 12
#> n n_pred n_truth precision recall f1 covering hausdorff rand_index
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 300 2 2 1 1 1 1 0 1
#> # ℹ 3 more variables: annotation_error <int>, mae_matched <dbl>,
#> # rmse_matched <dbl>
cpt_metrics(c(101, 205), c(100, 200), n = 300, margin = 5)
#> # A tibble: 1 × 12
#> n n_pred n_truth precision recall f1 covering hausdorff rand_index
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 300 2 2 1 1 1 0.961 5 0.941
#> # ℹ 3 more variables: annotation_error <int>, mae_matched <dbl>,
#> # rmse_matched <dbl>