How to hash a large object (dataset) in Python?

Question

I would like to calculate a hash of a Python class containing a dataset for Machine Learning. The hash is meant to be used for caching, so I was thinking of md5 or sha1. The problem is that most of the data is stored in NumPy arrays; these do not provide a __hash__() member. Currently I do a pickle.dumps() for each member and calculate a hash based on these strings. However, I found the following links indicating that the same object could lead to different serialization strings:

• Hash of None varies per machine
• Pickle.dumps not suitable for hashing

What would be the best method to calculate a hash for a Python class containing Numpy arrays?

Answer 1

What's the format of the data in the arrays? Couldn't you just iterate through the arrays, convert them into a string (via some reproducible means) and then feed that into your hash via update?

e.g.

import hashlib
m = hashlib.md5() # or sha1 etc
for value in array: # array contains the data
    m.update(str(value))

Don't forget though that numpy arrays won't provide __hash__() because they are mutable. So be careful not to modify the arrays after your calculated your hash (as it will no longer be the same).

Answer 2

Thanks to John Montgomery I think I have found a solution, and I think it has less overhead than converting every number in possibly huge arrays to strings:

I can create a byte-view of the arrays and use these to update the hash. And somehow this seems to give the same digest as directly updating using the array:

>>> import hashlib
>>> import numpy
>>> a = numpy.random.rand(10, 100)
>>> b = a.view(numpy.uint8)
>>> print a.dtype, b.dtype # a and b have a different data type
float64 uint8
>>> hashlib.sha1(a).hexdigest() # byte view sha1
'794de7b1316b38d989a9040e6e26b9256ca3b5eb'
>>> hashlib.sha1(b).hexdigest() # array sha1
'794de7b1316b38d989a9040e6e26b9256ca3b5eb'

Answer 3

array.data is always hashable, because it's a buffer object. easy :) (unless you care about the difference between differently-shaped arrays with the exact same data, etc.. (ie this is suitable unless shape, byteorder, and other array 'parameters' must also figure into the hash)