elbow_helper.metrics module

Phase 4 — per-candidate metrics and basic rejection filters.

Given a raw KneeCandidate (location + the difference curve it came from), attach its prominence, local-noise estimate, prominence-to-noise ratio and boundary distance, then decide whether it clears the cheap structural filters before any expensive stability analysis.

Author

Warith Harchaoui, <warith.harchaoui@deraison.ai>

elbow_helper.metrics.evaluate_candidate(candidate, y_difference, threshold_index, y_scaled)[source]

Fill in a candidate’s prominence, noise, ratio and boundary distance.

Parameters:

  • candidate (KneeCandidate) – The candidate to annotate (mutated in place and returned).

  • y_difference (numpy.ndarray) – The difference curve the candidate was found on.

  • threshold_index (int) – Index of the generating peak on y_difference.

  • y_scaled (numpy.ndarray) – The (unsmoothed) scaled signal, for the robust noise estimate.

Returns:

The same object, with metric fields populated.

Return type:

KneeCandidate

elbow_helper.metrics.passes_basic_filters(candidate, n, config)[source]

Return True iff a candidate survives the cheap structural filters.

Sets candidate.rejected to a reason code when it fails. Rejections: within the boundary margin, too few points on one side, within half a smoothing window of an end, weak prominence, or weak prominence-to-noise.

Parameters:
Returns:

Whether the candidate passes.

Return type:

bool