test: add diversity algorithm tests (60 → 93) (#11)
Tests cover the core ML pipeline algorithms in diversity.py — previously untested. No network or model dependencies; all pure-function or numpy-only paths: - Face bbox and confidence extraction from Immich metadata, including person_id filtering and missing-data edge cases - Face crop scaling: verifies bbox coordinates are correctly scaled when the thumbnail dimensions differ from the metadata image dimensions - Near-duplicate dedup: removal below cosine threshold, quality-score preference between duplicates, zero-quality-score treated as zero not missing (falsy bug guard) - K-Medoids: correct medoid count, distinctness, valid index range, and full-N edge case - Adaptive threshold: positive output, floor at 0.05 for identical embeddings, single-point, scales with embedding spread - Time-spread fallback: exact count, all-under-limit passthrough, auto→30 default, first/last inclusion - Cluster-aware selection: exact limit, subset invariant, auto-stop on tight cluster, hard-example confidence weighting accepted
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