Module metrics 

Metric functions — numbers computed from matches.

Every item here is a free function over flat arrays, callable without constructing an evaluator:

use hotcoco::metrics::counts::average_precision;

let scores  = [0.9, 0.8, 0.3];
let matched = [true, false, true];
let ap = average_precision(&scores, &matched, None, 3, &[0.0, 0.5, 1.0]);

This is the same shape sklearn.metrics and torchmetrics.functional use, and for the same reason: a metric is a pure function of its inputs, so tying it to an evaluator object only makes it harder to reach.

metrics vs primitives

The split is by what a function produces, not by which family calls it:

	Produces	Contains
primitives	matches and similarities	sim, greedy, assign
metrics	numbers from matches	counts, calibration, confusion, bootstrap

primitives::greedy::greedy_match_masked decides which detection pairs with which ground truth. metrics::counts::average_precision turns that decision into a number. Nothing here matches; nothing there scores.

tests/architecture.rs enforces the direction of the dependency: metrics may not import from a family driver such as detection, and primitives may not import from metrics.

Taking (scores, matched) rather than a family-specific struct is what makes these reusable across families: detection produces those arrays from eval_imgs, tracking and panoptic will produce them from their own match records, and the function does not know which called it.

Degenerate-input convention

These free functions share one policy, the same as primitives:

• Mismatched parallel-array lengths are a programmer error and panic via assert! with a message naming both lengths. Nothing silently truncates, no-ops, or degrades (primitives::assign::lsap set the pattern). Each function’s # Panics section states its checks.
• Empty input is not an error — it produces the documented empty-set value (0.0, an empty Vec, an all-zero matrix), because “no detections” is a legitimate evaluation state, not a bug.

Stability

Provisional through 1.x, like primitives: public so the family drivers and Python can share them, but not frozen until 1.4. Expect additive change — new functions, and the tracking count vocabulary in counts — rather than reshaping of what is here.

Modules

bootstrap

Bootstrap confidence intervals over any resampled statistic.

calibration

Confidence calibration — reliability bins, ECE, and MCE.

confusion

Confusion counts over matched prediction/ground-truth pairs.

counts

Count aggregation and metric formulas.

Functions

is_computed

Whether a metric value was actually computed, as opposed to carrying the crate’s -1.0 “not computed for this configuration” sentinel.

is_missing

The complement of is_computed: v is the “not computed” sentinel.