tsseg.metrics.BidirectionalCovering

class tsseg.metrics.BidirectionalCovering(*, convert_labels_to_segments=False, aggregation='harmonic', **kwargs)

Bidirectional extension of the classical Covering metric.

The classical Covering score only evaluates how well predicted segments cover the ground-truth segmentation. However, this directionality means that long predicted segments that cover the truth sparsely may still obtain a high score, even when the prediction introduces substantial over-segmentation.

The bidirectional variant evaluates coverage in both directions:

• ground_truth_covering mirrors the traditional definition where each ground-truth interval is weighted by its duration and matched to the best overlapping predicted interval via Intersection over Union (IoU).

• prediction_covering swaps the roles. Each predicted segment is weighted by its duration and matched to the best ground-truth overlap.

The two directional scores are then aggregated using an F1-style harmonic mean by default. Alternative aggregation strategies (geometric, arithmetic or min) can be selected via the aggregation argument. The resulting metric rewards segmentations that both cover the truth and avoid excessive over-segmentation.

Parameters:

• convert_labels_to_segments (bool) – When True, the inputs are interpreted as label sequences and will be converted to change-points via tsseg.metrics.change_point_detection.labels_to_change_points().

• aggregation (str) – Name of the aggregation strategy used to combine the two directional covering scores. Supported values are "harmonic" (default), "geometric", "arithmetic" and "min".

• kwargs – Forwarded to tsseg.metrics.base.BaseMetric.

__init__(*, convert_labels_to_segments=False, aggregation='harmonic', **kwargs)

Initializes the metric with optional parameters.

Methods

__init__(*[, convert_labels_to_segments, ...])	Initializes the metric with optional parameters.
compute(y_true, y_pred)	Computes the value of the metric.