tsseg.metrics.GaussianF1Score

class tsseg.metrics.GaussianF1Score(*, sigma_fraction=0.01, min_sigma=1.0, adaptive_sigma=False, convert_labels_to_segments=False)

Gaussian-weighted alternative to the classic F1 score.

The metric operates in three conceptual steps:

1. Preparation – convert optional label sequences into change point

   lists, remove boundary markers, and infer the series length.

2. Gaussian matching – every true change point is associated with a

   Gaussian of width sigma_fraction * n (clamped below by min_sigma). Predictions are rewarded according to that shared kernel and a greedy assignment keeps the best non-overlapping pairs.

3. Soft precision & recall – derive precision and recall from the sum of

   Gaussian rewards, yielding a fuzzy F1 in \([0, 1]\).

Special cases are handled explicitly:

• No ground-truth change point – if the data really is stationary and no

  change points are predicted either, we return the perfect score 1.0. Conversely, predicting spurious changes yields a zero score.

• Single change point – the Gaussian spread still follows the global

  fraction, ensuring a consistent reward scale across all events.

__init__(*, sigma_fraction=0.01, min_sigma=1.0, adaptive_sigma=False, convert_labels_to_segments=False)

Initializes the metric with optional parameters.

Methods

__init__(*[, sigma_fraction, min_sigma, ...])	Initializes the metric with optional parameters.
compute(y_true, y_pred)	Return the Gaussian-weighted precision, recall, and F1 score.