Partial Add Reduction Loses Index Combinations

[BruceChurch]

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.

Thanks,
Bruce

import numpy as np
import pandas as pd
import sparse 

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)
print df.drop_duplicates().shape
# how many unique combos is the 2 slots of z? 
df2=pd.DataFrame(z.coords.T)
print df2.drop_duplicates().shape
# Shouldn't these match? 

[hameerabbasi]

@BruceChurch, your test is a little off. This line:

df = pd.DataFrame(coords[:2].T)

Should be

df = pd.DataFrame(coords[2:].T)

Since we have to check the uniques over the axes that we are NOT summing over. That said, PR #83 fixes this issue. Would you mind testing it?

[BruceChurch]

Yes this fixes the problem. Thanks!