Closed LJ191021 closed 10 months ago
This was fixed in main; but I have made a new release that should contain the fix. It should be on PyPI soon.
i modify the codes showed in the figure below and it could run successfully.
i wonder that if this modification will affect the clustering results.
the error below happened when i just import fast_hdbscan.
Traceback (most recent call last): File "F:\LZQ\jingsai\rr.py", line 1, in
import fast_hdbscan
File "E:\anaconda3\lib\site-packages\fast_hdbscan__init.py", line 7, in
HDBSCAN(allow_single_cluster=True).fit(random_data)
File "E:\anaconda3\lib\site-packages\fast_hdbscan\hdbscan.py", line 217, in fit
) = fast_hdbscan(clean_data, return_trees=True, **kwargs)
File "E:\anaconda3\lib\site-packages\fast_hdbscan\hdbscan.py", line 149, in fast_hdbscan
edges = parallel_boruvka(
File "E:\anaconda3\lib\site-packages\fast_hdbscan\boruvka.py", line 270, in parallel_boruvka
new_edges = merge_components(components_disjoint_set, candidate_indices, candidate_distances, point_components)
File "E:\anaconda3\lib\site-packages\numba\core\dispatcher.py", line 468, in _compile_for_args
error_rewrite(e, 'typing')
File "E:\anaconda3\lib\site-packages\numba\core\dispatcher.py", line 409, in error_rewrite
raise e.with_traceback(None)
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Cannot unify DictType[int32,Tuple(int64, int32, float32)] and DictType[int64,Tuple(int64, int32, float32)] for 'closure_locals dictcompv54vphi36_0', defined at E:\anaconda3\lib\site-packages\fast_hdbscan\boruvka.py (9)
File "E:\anaconda3\lib\site-packages\fast_hdbscan\boruvka.py", line 9: def merge_components(disjoint_set, candidate_neighbors, candidate_neighbor_distances, point_components): component_edges = {0: (0, np.int32(1), np.float32(0.0)) for i in range(0)} ^
During: typing of assignment at E:\anaconda3\lib\site-packages\fast_hdbscan\boruvka.py (9)
File "E:\anaconda3\lib\site-packages\fast_hdbscan\boruvka.py", line 9: def merge_components(disjoint_set, candidate_neighbors, candidate_neighbor_distances, point_components): component_edges = {0: (0, np.int32(1), np.float32(0.0)) for i in range(0)} ^