Testing Guidelines

Introduction

Until the 1.15 release, NumPy used the nose testing framework, it now uses the pytest framework. The older framework is still maintained in order to support downstream projects that use the old numpy framework, but all tests for NumPy should use pytest.

Our goal is that every module and package in SciPy and NumPy should have a thorough set of unit tests. These tests should exercise the full functionality of a given routine as well as its robustness to erroneous or unexpected input arguments. Long experience has shown that by far the best time to write the tests is before you write or change the code - this is test-driven development. The arguments for this can sound rather abstract, but we can assure you that you will find that writing the tests first leads to more robust and better designed code. Well-designed tests with good coverage make an enormous difference to the ease of refactoring. Whenever a new bug is found in a routine, you should write a new test for that specific case and add it to the test suite to prevent that bug from creeping back in unnoticed.

To run SciPy’s full test suite, use the following:

>>> import scipy
>>> scipy.test()

or from the command line:

$ python runtests.py

SciPy uses the testing framework from numpy.testing, so all the SciPy examples shown here are also applicable to NumPy. NumPy’s full test suite can be run as follows:

>>> import numpy
>>> numpy.test()

The test method may take two or more arguments; the first, label is a string specifying what should be tested and the second, verbose is an integer giving the level of output verbosity. See the docstring for numpy.test for details. The default value for label is ‘fast’ - which will run the standard tests. The string ‘full’ will run the full battery of tests, including those identified as being slow to run. If verbose is 1 or less, the tests will just show information messages about the tests that are run; but if it is greater than 1, then the tests will also provide warnings on missing tests. So if you want to run every test and get messages about which modules don’t have tests:

>>> scipy.test(label='full', verbose=2) # or scipy.test('full', 2)

Finally, if you are only interested in testing a subset of SciPy, for example, the integrate module, use the following:

>>> scipy.integrate.test()

or from the command line:

$python runtests.py -t scipy/integrate/tests

The rest of this page will give you a basic idea of how to add unit tests to modules in SciPy. It is extremely important for us to have extensive unit testing since this code is going to be used by scientists and researchers and is being developed by a large number of people spread across the world. So, if you are writing a package that you’d like to become part of SciPy, please write the tests as you develop the package. Also since much of SciPy is legacy code that was originally written without unit tests, there are still several modules that don’t have tests yet. Please feel free to choose one of these modules and develop tests for it as you read through this introduction.

Writing your own tests

Every Python module, extension module, or subpackage in the SciPy package directory should have a corresponding test_<name>.py file. Pytest examines these files for test methods (named test*) and test classes (named Test*).

Suppose you have a SciPy module scipy/xxx/yyy.py containing a function zzz(). To test this function you would create a test module called test_yyy.py. If you only need to test one aspect of zzz, you can simply add a test function:

def test_zzz():
    assert_(zzz() == 'Hello from zzz')

More often, we need to group a number of tests together, so we create a test class:

from numpy.testing import assert_, assert_raises

# import xxx symbols
from scipy.xxx.yyy import zzz

class TestZzz:
    def test_simple(self):
        assert_(zzz() == 'Hello from zzz')

    def test_invalid_parameter(self):
        assert_raises(...)

Within these test methods, assert_() and related functions are used to test whether a certain assumption is valid. If the assertion fails, the test fails. Note that the Python builtin assert should not be used, because it is stripped during compilation with -O.

Note that test_ functions or methods should not have a docstring, because that makes it hard to identify the test from the output of running the test suite with verbose=2 (or similar verbosity setting). Use plain comments (#) if necessary.

Labeling tests

As an alternative to pytest.mark.<label>, there are a number of labels you can use.

Unlabeled tests like the ones above are run in the default scipy.test() run. If you want to label your test as slow - and therefore reserved for a full scipy.test(label='full') run, you can label it with a decorator:

# numpy.testing module includes 'import decorators as dec'
from numpy.testing import dec, assert_

@dec.slow
def test_big(self):
    print 'Big, slow test'

Similarly for methods:

class test_zzz:
    @dec.slow
    def test_simple(self):
        assert_(zzz() == 'Hello from zzz')

Available labels are:

• slow: marks a test as taking a long time

• setastest(tf): work-around for test discovery when the test name is non conformant

• skipif(condition, msg=None): skips the test when eval(condition) is True

• knownfailureif(fail_cond, msg=None): will avoid running the test if eval(fail_cond) is True, useful for tests that conditionally segfault

• deprecated(conditional=True): filters deprecation warnings emitted in the test

• paramaterize(var, input): an alternative to pytest.mark.paramaterized

Easier setup and teardown functions / methods

Testing looks for module-level or class-level setup and teardown functions by name; thus:

def setup():
    """Module-level setup"""
    print 'doing setup'

def teardown():
    """Module-level teardown"""
    print 'doing teardown'


class TestMe:
    def setup():
        """Class-level setup"""
        print 'doing setup'

    def teardown():
        """Class-level teardown"""
        print 'doing teardown'

Setup and teardown functions to functions and methods are known as “fixtures”, and their use is not encouraged.

Parametric tests

One very nice feature of testing is allowing easy testing across a range of parameters - a nasty problem for standard unit tests. Use the dec.paramaterize decorator.

Doctests

Doctests are a convenient way of documenting the behavior of a function and allowing that behavior to be tested at the same time. The output of an interactive Python session can be included in the docstring of a function, and the test framework can run the example and compare the actual output to the expected output.

The doctests can be run by adding the doctests argument to the test() call; for example, to run all tests (including doctests) for numpy.lib:

>>> import numpy as np
>>> np.lib.test(doctests=True)

The doctests are run as if they are in a fresh Python instance which has executed import numpy as np. Tests that are part of a SciPy subpackage will have that subpackage already imported. E.g. for a test in scipy/linalg/tests/, the namespace will be created such that from scipy import linalg has already executed.

tests/

Rather than keeping the code and the tests in the same directory, we put all the tests for a given subpackage in a tests/ subdirectory. For our example, if it doesn’t already exist you will need to create a tests/ directory in scipy/xxx/. So the path for test_yyy.py is scipy/xxx/tests/test_yyy.py.

Once the scipy/xxx/tests/test_yyy.py is written, its possible to run the tests by going to the tests/ directory and typing:

python test_yyy.py

Or if you add scipy/xxx/tests/ to the Python path, you could run the tests interactively in the interpreter like this:

>>> import test_yyy
>>> test_yyy.test()

__init__.py and setup.py

Usually, however, adding the tests/ directory to the python path isn’t desirable. Instead it would better to invoke the test straight from the module xxx. To this end, simply place the following lines at the end of your package’s __init__.py file:

...
def test(level=1, verbosity=1):
    from numpy.testing import Tester
    return Tester().test(level, verbosity)

You will also need to add the tests directory in the configuration section of your setup.py:

...
def configuration(parent_package='', top_path=None):
    ...
    config.add_subpackage('tests')
    return config
...

Now you can do the following to test your module:

>>> import scipy
>>> scipy.xxx.test()

Also, when invoking the entire SciPy test suite, your tests will be found and run:

>>> import scipy
>>> scipy.test()
# your tests are included and run automatically!

Tips & Tricks

Creating many similar tests

If you have a collection of tests that must be run multiple times with minor variations, it can be helpful to create a base class containing all the common tests, and then create a subclass for each variation. Several examples of this technique exist in NumPy; below are excerpts from one in numpy/linalg/tests/test_linalg.py:

class LinalgTestCase:
    def test_single(self):
        a = array([[1.,2.], [3.,4.]], dtype=single)
        b = array([2., 1.], dtype=single)
        self.do(a, b)

    def test_double(self):
        a = array([[1.,2.], [3.,4.]], dtype=double)
        b = array([2., 1.], dtype=double)
        self.do(a, b)

    ...

class TestSolve(LinalgTestCase):
    def do(self, a, b):
        x = linalg.solve(a, b)
        assert_almost_equal(b, dot(a, x))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))

class TestInv(LinalgTestCase):
    def do(self, a, b):
        a_inv = linalg.inv(a)
        assert_almost_equal(dot(a, a_inv), identity(asarray(a).shape[0]))
        assert_(imply(isinstance(a, matrix), isinstance(a_inv, matrix)))

In this case, we wanted to test solving a linear algebra problem using matrices of several data types, using linalg.solve and linalg.inv. The common test cases (for single-precision, double-precision, etc. matrices) are collected in LinalgTestCase.

Known failures & skipping tests

Sometimes you might want to skip a test or mark it as a known failure, such as when the test suite is being written before the code it’s meant to test, or if a test only fails on a particular architecture.

To skip a test, simply use skipif:

import pytest

@pytest.mark.skipif(SkipMyTest, reason="Skipping this test because...")
def test_something(foo):
    ...

The test is marked as skipped if SkipMyTest evaluates to nonzero, and the message in verbose test output is the second argument given to skipif. Similarly, a test can be marked as a known failure by using xfail:

import pytest

@pytest.mark.xfail(MyTestFails, reason="This test is known to fail because...")
def test_something_else(foo):
    ...

Of course, a test can be unconditionally skipped or marked as a known failure by using skip or xfail without argument, respectively.

A total of the number of skipped and known failing tests is displayed at the end of the test run. Skipped tests are marked as 'S' in the test results (or 'SKIPPED' for verbose > 1), and known failing tests are marked as 'x' (or 'XFAIL' if verbose > 1).

Tests on random data

Tests on random data are good, but since test failures are meant to expose new bugs or regressions, a test that passes most of the time but fails occasionally with no code changes is not helpful. Make the random data deterministic by setting the random number seed before generating it. Use either Python’s random.seed(some_number) or NumPy’s numpy.random.seed(some_number), depending on the source of random numbers.

Alternatively, you can use Hypothesis to generate arbitrary data. Hypothesis manages both Python’s and Numpy’s random seeds for you, and provides a very concise and powerful way to describe data (including hypothesis.extra.numpy, e.g. for a set of mutually-broadcastable shapes).

The advantages over random generation include tools to replay and share failures without requiring a fixed seed, reporting minimal examples for each failure, and better-than-naive-random techniques for triggering bugs.