Incorrect behavior for Moran_Local and self-neighbors

[ljwolf]

Moran_Local assumes no self-neighbors when running conditional permutations, but neither checks nor appropriately removes them.

This happens when we do conditional permutation, so I think it means that there's some issue with trimming the weights w[i] in Line 931. Basically, you end up getting one more observation than you want in w[i]

Suggested fix:

force remove all self-weighting when conducting any kind of conditional randomization in esda.

To reproduce:

import pysal, numpy
adjacency = numpy.array([[1,1,0],
                         [1,1,1],
                         [0,1,1]])
data = [1,2,3]
pysal.explore.esda.Moran_Local(data, adjacency)

[jeffcsauer]

This is what I am getting when I attempt to reproduce, but after experimenting a bit I'm not sure it's the same scenario you are getting. Is this the 'correct' incorrect output?

>>> import pysal, numpy
>>> adjacency = numpy.array([[1,1,0],[1,1,1],[0,1,1]])
>>> data = [1,2,3]
>>> pysal.explore.esda.Moran_Local(data, adjacency)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\jeffe\AppData\Local\Programs\Python\Python38-32\lib\site-packages\esda\moran.py", line 863, in __init__
    w.transform = transformation
AttributeError: 'numpy.ndarray' object has no attribute 'transform'

[ljwolf]

Hey @jeffcsauer, yeah that's my fault for missing a step in the reproducible example!

Sorry!

This should be it.

from pysal.lib import weights
from pysal.explore import esda
from scipy import sparse

adjacency = sparse.csc_matrix([[1,1,0],
                               [1,1,1],
                               [0,1,1]])
w = weights.WSP(adjacency).to_W()
data = [1,2,3]

esda.Moran_Local(data, w)

[jeffcsauer]

@ljwolf no worries at all! Thanks for the quick follow-up and clarification 👍

[sjsrey]

To clarify, is the use case actually requiring self-neighbors?
Or, is this a request to add error-checking to rule out self-neighbors?

[ljwolf]

@sjsrey this is further discussed over in #133. The crand engine makes this a particularly good time to deal with this, since we need to handle self-neighbors for Gi-star & LOSH

I propose (like Gi-star), we allow self-neighbors in local statistics and correctly handle their conditional randomization: hold their values constant and shuffle non-diagonal elements.

If we want to warn/exception on users passing self-weighted W in Moran_Local, we also need to do that in:

• all Moran* statistics
• Geary
• Lee-type statistics
• geosilhouettes