Numpy : check if any array in 3D array is in another shorter 3D array with duplicates

I’ve got a Numpy array like this one :

source = np.array([[[0,0,0],[0,0,1],[0,1,0],[1,0,0],[1,0,1],[1,1,0],[1,1,1]]])

And I’m trying to compare it to an other array, which has shorter Axis2 and duplicates in Axis3 :

values = np.array([[[0,1,0],[1,0,0],[1,1,1],[1,1,1],[0,1,0]]])

My goal is to have an array of booleans as long as the longest :

[False, False,True,True,False,False,True]

I’ve tried these command :

np.isin(source,values).all(axis=2)

But it displays an array of seven True. A function like numpy.in1d() seemed to be a good option, but I’ve didn’t achieve to adapt it for 3D arrays.

>Solution :

One way:

np.in1d(np.apply_along_axis(''.join, 2, source.astype(str)), 
        np.apply_along_axis(''.join, 2, values.astype(str)))

array([False, False,  True,  True, False, False,  True])

Another way, though might be memory intensive:

(source.transpose(1,0,2) == values).all(2).any(1)
array([False, False,  True,  True, False, False,  True])