Using numpy.testing functions with unittest

Question

I am using numpy.testing.assert_almost_equal in a unittest environment - but I am not sure what the right way to combine numpy and unittest is.

My first approach was to use assertTrue from unittest in combination with a is None comparison like so:

from unittest import TestCase
import numpy as np

class TestPredict(TestCase):
    def test_succeeding(self):
        self.assertTrue(
            np.testing.assert_almost_equal(1, 0.9999999999999) is None
        )
    def test_failing(self):
        self.assertTrue(
            np.testing.assert_almost_equal(1, 0.9) is None
        )

This gives the correct test results, but it is a bit hacky and it bloats the test code.

A simpler approach is the following:

from unittest import TestCase
import numpy as np

class TestPredict(TestCase):
    def test_succeeding(self):
        np.testing.assert_almost_equal(1, 0.9999999999999)
    def test_failing(self):
        np.testing.assert_almost_equal(1, 0.9)

This code also returns the correct test statistics as the above, but it is much more readable. The only downside I see with this is that pylint complains about the "R0201 method could be a function" message. Can this become an issue?

PS: I checked multiple posts here on SO that seemed related but didn't answer my specific question about the integration of unittest and numpy testing. (e.g. https://stackoverflow.com/a/4319870/6018688 talks bout catching Exceptions inside unittests. This seems bo be wrong or just simply an overkill.)

In your test, you don't need any setUp function or other function of that kind, so the class construction is not usefull. You can directly put your 2 functions out of the class structure. — pyOliv
– pyOliv, Commented May 22, 2020 at 11:01
Thank you for this comment. I will indeed need setUp to compute the value close to one but I also stand reminded of the direct use of functions with pytest as @mrbean-bremen mentioned in the second part of his answer. — fabianegli
– fabianegli, Commented May 23, 2020 at 12:45

MrBean Bremen · Accepted Answer · 2020-05-22 19:37:40Z

If you are in a unittest enviroment, your second try is perfectly ok. If you don't want the pylint warnings, you can make static functions from the methods:

from unittest import TestCase
import numpy as np

class TestPredict(TestCase):
    @staticmethod
    def test_succeeding():
        np.testing.assert_almost_equal(1, 0.9999999999999)

    @staticmethod
    def test_failing():
        np.testing.assert_almost_equal(1, 0.9)

Note that pylint is basically just warning about the unused self parameter - this would not cause any issue except from the warning itself.

If you can use pytest instead, the code gets even cleaner, because you don't have to derive from a test case class:

import numpy as np

def test_succeeding():
    np.testing.assert_almost_equal(1, 0.9999999999999)

def test_failing():
    np.testing.assert_almost_equal(1, 0.9)

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