Changepoint accuracy metrics

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.

Usage

cpt_metrics(pred, truth, n, margin = 5)

Arguments

pred: Predicted changepoint indices (integer vector).
truth: Ground truth changepoint indices (integer vector).
n: Length of the series.
margin: Tolerance margin for matching (default 5).

Value

A tibble with one row and the columns below. “Higher” and “lower” mark which direction is better, the same directions cpt_benchmark() ranks by.

n, n_pred, n_truth: the series length and the two changepoint counts, after out-of-range indices are dropped.
precision, recall, f1: higher is better. The fraction of predictions that matched a truth, the fraction of truths that were matched, and their harmonic mean, all under the one-to-one matching within margin, so they score whether a change was found, not how precisely it was located.
covering: higher is better, in \([0, 1]\). The segmentation covering metric: each true segment's best Jaccard overlap with a predicted segment, averaged weighted by segment length. Unlike F1 it needs no margin and degrades smoothly with location error.
hausdorff: lower is better, in observations. The largest distance from any changepoint on either side to the nearest one on the other: a worst-case location error, so one badly placed changepoint dominates it.
rand_index: higher is better. The adjusted Rand index between the two segment labellings: 1 for identical partitions, 0 for chance agreement, and negative for worse than chance.
annotation_error: lower is better. A count difference only: abs(n_pred - n_truth). It says nothing about location, so a segmentation with the right number of changepoints in entirely the wrong places scores a perfect 0. Read it beside covering or hausdorff, never alone.
mae_matched, rmse_matched: lower is better, in observations. Mean absolute and root-mean-square location error over the matched pairs only, so they describe how well the changepoints that were found are placed and ignore the ones that were missed. NA when nothing 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). 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.

Degenerate cases, and which way each metric resolves them

Three of the twelve columns are ratios with a zero denominator when one side is empty, and the row does not resolve them all the same way, so the conventions are worth stating rather than inferring from a benchmark table:

Both empty. The segmentation is exactly right: precision, recall, f1, covering and rand_index are all 1 and annotation_error is 0. (A metric that scored this 0 was a 0.4.0 bug.)
One side empty. precision and recall are 0 rather than NA (finding nothing where there was a change, and finding a change where there was none, both score badly), and so is f1. rand_index is 0 for the same reason.
Nothing matched. hausdorff, mae_matched and rmse_matched are NA, because they are distances and there is no pair to measure. They are also NA whenever either side is empty. This is the one place the row mixes conventions: an all-wrong answer gives f1 = 0 and mae_matched = NA in the same row. cpt_benchmark() treats an NA as the worst rank rather than dropping it.

An index outside 1..(n - 1) is dropped with a warning: locations follow the "left" convention, and an out-of-range one would corrupt the partition metrics rather than merely miss.

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>