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Is this correct behavior for a partial add reduction? Using the canonical example I added a reduction over the first two indices. And the results look strange in that the first index of the resulting 2D sparse matrix are all zeros. Here is a simple example illustrating the point.
import numpy as np
import pandas as pd
n = 1000
ndims = 4
nnz = 1000000
coords = np.random.randint(0, n - 1, size=(ndims, nnz))
data = np.random.random(nnz)
x = sparse.COO(coords, data, shape=((n,)*ndims))
z = x.sum(axis=(0,1))
# how many unique combos is the last 2 slots of x?
df = pd.DataFrame(coords[:2].T)
# how many unique combos is the 2 slots of z?
# Shouldn't these match?
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