Add sparse matrix and edge case test coverage for random landmarking with precomputed matrices

[Copilot]

Addresses review feedback on PR #88 by adding comprehensive test coverage for sparse matrices and edge cases in random landmarking with precomputed matrices.

Changes

• Sparse precomputed affinity test: Validates CSR sparse affinity matrices trigger the correct code path (lines 1210-1217 in graphs.py)
• Sparse precomputed distance test: Validates CSR sparse distance matrices work correctly through distance-to-affinity conversion
• Zero affinity edge case test: Validates RuntimeWarning is raised when samples have zero affinity to all randomly selected landmarks (tests lines 1223-1228 in graphs.py)
• Comment clarity improvement: Updated graphs.py:1207 to clarify that kernel contains affinities regardless of precomputed type

# Now tested with sparse matrices
affinity_sparse = sp.csr_matrix(affinity)
G = graphtools.Graph(
    affinity_sparse,
    precomputed="affinity",
    n_landmark=3,
    random_landmarking=True,
)

All 21 random landmarking tests pass.

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[coveralls]

Pull Request Test Coverage Report for Build 20349013595

Details

• 0 of 0 changed or added relevant lines in 0 files are covered.
• No unchanged relevant lines lost coverage.
• Overall coverage remained the same at 0.0%

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Totals
Change from base Build 20348469484:	0.0%
Covered Lines:	0
Relevant Lines:	0

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💛 - Coveralls

[MattScicluna]

Code lgtm. But not sure why coveralls failing.

[Copilot]

Pull request overview

This PR adds comprehensive test coverage for sparse matrices and edge cases in random landmarking with precomputed matrices. The changes address review feedback from PR #88 by ensuring that sparse precomputed affinity/distance matrices and zero-affinity edge cases are properly tested.

Key Changes:

• Added test for sparse CSR precomputed affinity matrices with random landmarking
• Added test for sparse CSR precomputed distance matrices with random landmarking
• Added test for zero affinity edge case that validates proper warning emission
• Improved code comment clarity in graphs.py to better explain kernel usage

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.

File	Description
test/test_random_landmarking.py	Added three new test functions covering sparse precomputed matrices (affinity and distance) and zero affinity warning edge case, plus sp import
graphtools/graphs.py	Updated comment to clarify that kernel contains affinities computed from precomputed matrices regardless of precomputed type

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[Copilot]

The test should use the assert_warns_message helper function available in load_tests instead of manual warning capture with warnings.catch_warnings. This is the established pattern in the codebase for testing warnings (see test_exact.py line 96). Replace this manual warning handling with a cleaner approach using the helper.