seg_rand — EMPROOF `score` op

• Data kinds: labels2d × labels2d → table

• Call: import fullseye as fs; fs.ledger.seg_rand(a, b, ignore_label=None) (to call the implementation directly, import segcompare; segcompare.seg_rand(a, b, ignore_label=None); from the registry, opsemproof.get("seg_rand"))

Usage

Rand index, adjusted Rand index and the adapted Rand error (CREMI / SNEMI).

With n_ij the contingency table, a_i / b_j its row and column sums and N the pixel count:

• rand_index = fraction of pixel pairs on which the two agree (same/different);

• adjusted_rand_index = Hubert & Arabie's chance-corrected version (1 = identical, about 0 = independent);

• adapted_rand_error = 1 - F over pairs of distinct pixels: pair precision = (sum n_ij^2 - N) / (sum b_j^2 - N) — of the pairs the candidate puts together, the fraction the truth also puts together — and recall = (sum n_ij^2 - N) / (sum a_i^2 - N) (Arganda-Carreras et al. 2015). The - N removes each pixel's pair with itself. scikit-image's adapted_rand_error returns the same error, but its precision is divided by the truth pairs (its code sums the rows of the truth-by-test table), i.e. it is this recall; its docstring says otherwise.

Raises ValueError: as seg_contingency; fewer than 2 counted pixels.

Detailed usage guide

• emproof family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_em_split_merge_score — py -3.11 examples/poc_em_split_merge_score.py

Ops the type connects to (they accept `table` as input)

seg_synapse_partners · seg_wiring_variation · seg_synapse_nri · seg_wiring_exposure

Same category (`score`)

seg_contingency · seg_variation_of_information

*Provenance: segcompare.py — EMPROOF operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

seg_rand — EMPROOF score op