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name: domain-adaptation description: "Use POT optimal-transport domain-adaptation estimators, mappings, JCPOT target-shift adaptation, color/image transfer recipes, optional DR/WDA routes, and nearest Brenier potentials." disable-model-invocation: true metadata: disco-role: operating license: MIT

POT Domain Adaptation

Use this sub-skill when the task is to align source and target sample distributions with POT domain-adaptation estimators: EMDTransport, SinkhornTransport, class-regularized Sinkhorn transports, MappingTransport, LinearTransport, LinearGWTransport, UnbalancedSinkhornTransport, JCPOTTransport, NearestBrenierPotential, ot.mapping map estimators, image/color-transfer recipes, or optional ot.dr WDA/EWCA workflows.

Read references/api-reference.md to choose APIs, preserve verified signatures and defaults, use the correct fit/transform argument names, validate attributes such as cost_, coupling_, mapping_, A_, B_, proportions_, phi, and G, and decide which optional dependencies are required.

Read references/workflows.md for copy-pasteable workflows covering deterministic 2D EMD/Sinkhorn adaptation, class-regularized transport, semi-supervised labels with missing label -1, MappingTransport and linear Gaussian maps, multi-source JCPOT, RGB image/color adaptation from user-provided arrays, optional WDA/EWCA, and optional nearest Brenier potential usage.

Read references/troubleshooting.md when Xs/Xt/ys/yt are missing or misnamed, array shapes or labels are invalid, normalization or out_of_sample_map choices fail, convergence stalls, optional cvxpy/scikit-learn/autograd/pymanopt dependencies are missing, JCPOT source lists are malformed, or image/color data has the wrong shape or range.

Run scripts/domain_adaptation_smoke.py after installing POT to exercise deterministic NumPy-only estimator, mapping, JCPOT, and dependency-probe fixtures without plotting, downloads, native test execution, original checkout access, or optional dependency installation:

python scripts/domain_adaptation_smoke.py --case all --json

Route low-level balanced solver math to core-solvers, unbalanced/partial mass modeling fundamentals to unbalanced-partial, GW/FGW graph modeling and optional GNN layer details to gromov, optional backend or accelerator installation to backend-and-batch, and large-scale approximation families to sliced-gaussian-large-scale unless the current task is specifically an OT domain-adaptation estimator or map.