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name: supervised-density description: "Supervised, semi-supervised, densMAP, clustering, and outlier workflows for umap-learn." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

supervised-density

Use this sub-skill when labels, partial labels, density preservation, or clustering/outlier interpretation are central to a umap.UMAP task.

Route here for

• Supervised UMAP.fit(X, y=...) or fit_transform(X, y=...).
• Semi-supervised class labels, including the categorical -1 unlabeled convention.
• Choosing target_metric, target_weight, target_n_neighbors, and target_metric_kwds.
• densmap=True, dens_lambda, dens_frac, dens_var_shift, and output_dens=True.
• UMAP-assisted KMeans/HDBSCAN clustering, LOF-style outlier review, and exploratory-analysis caveats.

Route elsewhere

• Base estimator mechanics, transform/inverse/update, sparse/precomputed data, and general distance metrics: use the core-embedding sub-skill.
• Plot rendering and umap.plot: use the plotting-diagnostics sub-skill.
• Aligned multi-slice embeddings: use the aligned-composition sub-skill.
• TensorFlow/Keras ParametricUMAP: use the parametric-umap sub-skill.

Read first

• Supervised and density workflows
• Target and densMAP API reference
• Troubleshooting

Safe smoke helper

Run the bundled helper from this sub-skill directory for a no-network toy-data check:

python scripts/supervised_density_smoke.py --help

Use options such as --partial-label-fraction, --densmap, --output-dens, --cluster-method kmeans, and --outlier-check to exercise harder cases.

Operating cautions

• Labels guide topology; they do not guarantee better scientific structure.
• densMAP preserves relative local-density information more than standard UMAP, but costs extra runtime and has transform/inverse limitations.
• Clusters and outliers from a UMAP embedding are exploratory until validated against labels, stability, original-space neighbors, or domain evidence.