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As mentioned in this issue here scipy sparse matrix class has little to no functions available from the Dask Array module. For example, raising TTypeError: _cs_matrix.sum() got an unexpected keyword argument 'keepdims' when using da.sum(sparse_matrix, axis=0) or sparse_matrix.sum()
When using CountVectorizer and HashingVectorizer both return blocks of scipy.sparse_csr.csr_matrix data type.
To interact with those blocks, one has to do a change with the sparse COO module. See https://docs.dask.org/en/latest/array-sparse.html this fixed a problem i was having and trying to correct for multiple hours.
Default datatype should be sparse_coo.core.COO even at the cost if increasing depedencies, due to the fact that the result would be more dask-like and managaeable.
The text was updated successfully, but these errors were encountered:
As mentioned in this issue here scipy sparse matrix class has little to no functions available from the Dask Array module. For example, raising
TTypeError: _cs_matrix.sum() got an unexpected keyword argument 'keepdims'
when usingda.sum(sparse_matrix, axis=0)
orsparse_matrix.sum()
When using CountVectorizer and HashingVectorizer both return blocks of scipy.sparse_csr.csr_matrix data type.
To interact with those blocks, one has to do a change with the sparse COO module. See https://docs.dask.org/en/latest/array-sparse.html this fixed a problem i was having and trying to correct for multiple hours.
Default datatype should be sparse_coo.core.COO even at the cost if increasing depedencies, due to the fact that the result would be more dask-like and managaeable.
The text was updated successfully, but these errors were encountered: