Comparing Numpy Float Arrays In Unit Tests

What is the best way to implement a unittest that compares two numpy float arrays. I've tried unittest.assertEqual() but didn't work for float arrays because float are never 100% e

Solution 1:

If you are using numpy anyway, why not use the numpy testing functions?

numpy.testing.assert_array_almost_equal

and

numpy.testing.assert_array_almost_equal_nulp

These also handles NaN's fine, check shape, etc.

Solution 2:

Try

self.assertTrue(numpy.allclose(array1, array2, rtol=1e-05, atol=1e-08))

The allclose function from the numpy module, checks whether two arrays are the same within machine precision a given relative and absolute tolerance . rtol and atol are optional parameters with default values as given above.

Thanks to @DSM for correcting me.

Solution 3:

There is a version that can compare two arrays, which of course requires that numpy arrays behave properly, i.e. that they have a len() and that they allow square brackets to access elements. Now, concerning rounding errors, there is the possibility to define a delta or a range, which you could use, but I don't think this allows the use on arrays.

I'm afraid you'll have to roll your own.