silhouette_samples: ragged/NaN-padded input handled inconsistently across metric branches

Problem

tslearn.clustering.silhouette_samples handles ragged (NaN-padded) time-series input inconsistently across metric branches:

• metric="euclidean" raises a raw ValueError: Input X contains NaN.
• metric="dtw" silently truncates NaNs and returns results.
• metric="softdtw" silently miscalculates over the NaN padding (mean shifts from 0.1795 to 0.1518 on ragged data) with no warning.

Suggested fix

Add a documented policy: either validate that all series are full-length (and raise a clear error for ragged input in every branch), or strip NaN padding consistently before all branches (including euclidean), and add tests covering ragged/NaN-padded input for every metric.

Metadata

Severity: P1
Confidence: 75
Reviewer(s): adversarial (ce-code-review run 20260821-221628-4e205155 on PR #703)
Finding ID: tslearn/clustering/utils.py:358 ragged and NaN-padded time-series input

Start at tslearn/clustering/utils.py:358 and trace how silhouette_samples handles NaN-padded series in the euclidean, dtw, and softdtw branches. Add tests covering ragged input for every metric, then verify that all branches follow one documented policy and produce clear, consistent behavior.