Question

I use Python’s unittest module and want to check if two complex data structures are equal. The objects can be lists of dicts with all sorts of values: numbers, strings, Python containers (lists/tuples/dicts) and numpy arrays. The latter are the reason for asking the question, because I cannot just do

self.assertEqual(big_struct1, big_struct2)

because it produces a

ValueError: The truth value of an array with more than one element is ambiguous.
Use a.any() or a.all()

I imagine that I need to write my own equality test for this. It should work for arbitrary structures. My current idea is a recursive function that:

• tries direct comparison of the current “node” of arg1 to the corresponding node of arg2;
• if no exception is raised, moves on (“terminal” nodes/leaves are processed here, too);
• if ValueError is caught, goes deeper until it finds a numpy.array;
• compares the arrays (e.g. like this).

What seems a little problematic is keeping track of “corresponding” nodes of two structures, but perhaps zip is all I need here.

The question is: are there good (simpler) alternatives to this approach? Maybe numpy presents some tools for this? If no alternatives are suggested, I will implement this idea (unless I have a better one) and post as an answer.

P.S. I have a vague feeling that I might have seen a question addressing this problem, but I can’t find it now.

P.P.S. An alternative approach would be a function that traverses the structure and converts all numpy.arrays to lists, but is this any easier to implement? Seems the same to me.

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Edit: Subclassing numpy.ndarray sounds very promising, but obviously I don’t have both sides of the comparison hard-coded into a test. One of them, though, is indeed hardcoded, so I can:

• populate it with custom subclasses of numpy.array;
• change isinstance(other, SaneEqualityArray) to isinstance(other, np.ndarray) in jterrace‘s answer;
• always use it as LHS in comparisons.

My questions in this regard are:

1. Will it work (I mean, it sounds all right to me, but maybe some tricky edge cases will not be handled correctly)? Will my custom object always end up as LHS in the recursive equality checks, as I expect?
2. Again, are there better ways (given that I get at least one of the structures with real numpy arrays).

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Edit 2: I tried it out, the (seemingly) working implementation is shown in this answer.

Answer 1

So the idea illustrated by jterrace seems to work for me with a slight modification:

class SaneEqualityArray(np.ndarray):
    def __eq__(self, other):
        return (isinstance(other, np.ndarray) and self.shape == other.shape and 
            np.allclose(self, other))

Like I said, the container with these objects should be on the left side of the equality check. I create SaneEqualityArray objects from existing numpy.ndarrays like this:

SaneEqualityArray(my_array.shape, my_array.dtype, my_array)

in accordance with ndarray constructor signature:

ndarray(shape, dtype=float, buffer=None, offset=0,
        strides=None, order=None)

This class is defined within the test suite and serves for testing purposes only. The RHS of the equality check is an actual object returned by the tested function and contains real numpy.ndarray objects.

P.S. Thanks to the authors of both answers posted so far, they were both very helpful. If anyone sees any problems with this approach, I’d appreciate your feedback.