Comments (5)
This seems to be related to how Numba handles things. Hopefully there is a reasonable work-around. It will take some time to figure out the right way to handle this.
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Potentially I have a fix here. Hopefully this works... I won't have time to test it for a while as I'm travelling very soon.
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Thanks a lot, I'll give it a try!
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Thanks! This patch seems to work but only for the sparse case. To handle the dense case I changed renumbaify_tree
to the following, which seems to fix the issue:
def renumbaify_tree(tree):
if tree.hyperplanes[0].ndim == 1:
hyperplanes = numba.typed.List.empty_list(dense_hyperplane_type)
else:
hyperplanes = numba.typed.List.empty_list(sparse_hyperplane_type)
....
With this change I'm able to serialize a UMAP instance using joblib, and load it back.
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@adilosa's snippet also got me unstuck.
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Related Issues (20)
- `make_dense_tree()` with `angular=True` can segfault on poorly-behaved datasets HOT 1
- access distances in heap HOT 3
- Sample identifiers for semantic search HOT 2
- uint8 as internal data HOT 1
- Cosine metric - error "Negative values in data passed to precomputed distance matrix" HOT 2
- Question about covariance matrix used when using Mahalanobis distance
- Tests fail: E SystemError: initialization of _internal failed without raising an exception
- Newest version breaks with UMAP HOT 3
- Slice error using mac M1-max ARM HOT 6
- Exceedingly large amount of memory usage
- Very high memory usage HOT 4
- Specifying threshold in distance metrics
- API to save and load index from disk
- Minor bias in split selection? HOT 1
- true_angular is not a distance?
- TSSS missing a factor of 2
- Reverse diversification is actually forward diversification again
- pynndescent might break with next numba release HOT 3
- How to navigate pyinstaller HOT 1
- Querying the training set: runtime tradeoff for large k HOT 2
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