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SVD-initialized PARAFAC on sparse tensors failed because the sparse backend lacked argmax (axis) and index_update. Also validate that the imported `sparse` module provides the required functions so a wrong/shadowing module gives a clear ImportError instead of a bare AttributeError.
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Looks good to me, thank you! |
antonsoo
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Tested 85fd172 on CPU with PyData sparse 0.19.2: pytest tensorly/contrib/sparse tensorly/tenalg -q gives 291 passed.
Also checked reconstructed values beyond the new shape test: six sparse rank-1/2/3 tensors in float32/float64 match dense PARAFAC, and rank-4 PARAFAC of the Zachary karate-club adjacency matrix matches NumPy's truncated SVD (residual norm 7.346444141705379 versus 7.346444141705378). All seven decompositions fail on the parent a4f8954 with the missing argmax(axis=...) error. Twelve argmax comparisons covering implicit zeros, ties and negative axes also match NumPy on this head. No issue found in these paths.
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Related to #446.
argmax(withaxis) to the numpy.sparse backend, returning dense indices. Previouslyparafac(..., init="svd")on a sparse tensor failed withTypeError: Backend.argmax() got an unexpected keyword argument 'axis'insvd_flip.index_updatefor COO arrays (immutable), needed by the SVD init after the above.sparsemodule provides the functions the backend registers, and has__version__. A wrong or shadowingsparsemodule now raises a clearImportErrorinstead of a bareAttributeError(the symptom reported in parafac decomposition when registering sparse backend #446).argmax.pytest tensorly/contrib/sparse tensorly/tenalg: 291 passed (sparse 0.19.2).