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The last refactor commits removed topo.base.dists.pairwise_distances, topo.spectral.eigen.spectral_layout, kNN's return_instance/random_state kwargs and the graph_kernel assignment, but left callers and state checks behind, so import and fit() were broken. - kernels.py: delegate to sklearn.metrics.pairwise_distances - spectral/eigen.py: import Kernel lazily inside fit() to break the kernels <-> spectral.eigen circular import - projector.py: spectral init via EigenDecomposition with random fallback; silence pyright on the vendored pymde/torch glue - _pipeline + uom + eval + intrinsic_dim: drop removed kNN kwargs - topograph.py: remove the dead graph_kernel attribute and its fitted-state check (operators are already exposed as P_Z_/K_Z_/P_msZ_/K_msZ_) - uom.py: set P_Z_/P_msZ_ so the UoM path passes the fitted-state check; annotate uom_eigenvalues_*_list for mypy/pyright - update tests for the current APIs (LE instead of spectral_layout, standalone find_ideal_projection, new error messages, mixin contracts) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Duplicate (or float32-identical) points produce zero-distance edges that
eliminate_zeros() silently dropped from CSR kNN graphs, leaving rows with
fewer than k neighbors and breaking downstream consumers, e.g. the CkNN
candidate search ("Row 1 contains 45 distances, expected at least 46").
Clamp genuine off-diagonal zero distances to float32 tiny so they survive
sparse storage and later float32 casts, while self-loops are still
eliminated. Applies to both the sklearn and HNSWlib graph builders.
Also update the kNN tests for the simplified API (return_instance is gone)
and add a duplicate-point regression test for both backends.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The only remaining consumer of topo.base.dists was the euclidean_grad default in graph_utils.simplicial_set_embedding. umap-learn ships an identical numba-jitted implementation (umap.distances.euclidean_grad), so use that and delete the module. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- move ipykernel/ipywidgets/jupyterlab from runtime dependencies to the dev group; the library itself never imports them - stop requiring hnswlib and pacmap at runtime; they were both required and offered as extras, and the code already guards them via _optional - fix the optional-dependency hint for hnswlib to name the actual extra ([ann], not the nonexistent [hnswlib]) - drop ruff per-file-ignores pointing at deleted files - remove the unused uv-init main.py stub (no console script references it) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- add the standalone building blocks to the API reference: compute_kernel, eigendecompose, LE, graph_laplacian, diffusion_operator, automated_scaffold_sizing, find_ideal_projection, run_best_projection - give cknn_graph a full NumPy-style docstring with parameters, returns and the Berry & Sauer reference Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- _example_utils.py: use topo.spectral.LE for the spectral layout initialization (spectral_layout was removed) and drop the removed weight= argument from EigenDecomposition - re-sync the .ipynb files from their jupytext .py sources, which had drifted (the .py side carries the Colab setup cells and doc links) The demo pipeline (kNN -> cknn Kernel -> msDM -> LE init -> PaCMAP -> metrics) was run end-to-end to verify. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The TopOGraph tests kept passing while the standalone building blocks (kNN, Kernel, EigenDecomposition, LE, Projector, cknn_graph) were broken, because nothing exercised them composed outside the orchestrator. Add two integration tests mirroring the documented custom-pipeline path so that class of breakage is caught. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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