685 lines
22 KiB
ReStructuredText
685 lines
22 KiB
ReStructuredText
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.. _paramexamples:
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Parametrizing tests
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=================================================
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.. currentmodule:: _pytest.python
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``pytest`` allows to easily parametrize test functions.
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For basic docs, see :ref:`parametrize-basics`.
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In the following we provide some examples using
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the builtin mechanisms.
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Generating parameters combinations, depending on command line
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----------------------------------------------------------------------------
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.. regendoc:wipe
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Let's say we want to execute a test with different computation
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parameters and the parameter range shall be determined by a command
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line argument. Let's first write a simple (do-nothing) computation test:
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.. code-block:: python
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# content of test_compute.py
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def test_compute(param1):
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assert param1 < 4
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Now we add a test configuration like this:
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.. code-block:: python
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# content of conftest.py
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def pytest_addoption(parser):
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parser.addoption("--all", action="store_true", help="run all combinations")
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def pytest_generate_tests(metafunc):
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if "param1" in metafunc.fixturenames:
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if metafunc.config.getoption("all"):
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end = 5
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else:
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end = 2
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metafunc.parametrize("param1", range(end))
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This means that we only run 2 tests if we do not pass ``--all``:
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.. code-block:: pytest
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$ pytest -q test_compute.py
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.. [100%]
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2 passed in 0.12s
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We run only two computations, so we see two dots.
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let's run the full monty:
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.. code-block:: pytest
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$ pytest -q --all
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....F [100%]
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================================= FAILURES =================================
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_____________________________ test_compute[4] ______________________________
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param1 = 4
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def test_compute(param1):
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> assert param1 < 4
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E assert 4 < 4
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test_compute.py:4: AssertionError
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1 failed, 4 passed in 0.12s
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As expected when running the full range of ``param1`` values
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we'll get an error on the last one.
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Different options for test IDs
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------------------------------------
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pytest will build a string that is the test ID for each set of values in a
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parametrized test. These IDs can be used with ``-k`` to select specific cases
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to run, and they will also identify the specific case when one is failing.
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Running pytest with ``--collect-only`` will show the generated IDs.
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Numbers, strings, booleans and None will have their usual string representation
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used in the test ID. For other objects, pytest will make a string based on
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the argument name:
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.. code-block:: python
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# content of test_time.py
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import pytest
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from datetime import datetime, timedelta
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testdata = [
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(datetime(2001, 12, 12), datetime(2001, 12, 11), timedelta(1)),
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(datetime(2001, 12, 11), datetime(2001, 12, 12), timedelta(-1)),
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]
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@pytest.mark.parametrize("a,b,expected", testdata)
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def test_timedistance_v0(a, b, expected):
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diff = a - b
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assert diff == expected
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@pytest.mark.parametrize("a,b,expected", testdata, ids=["forward", "backward"])
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def test_timedistance_v1(a, b, expected):
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diff = a - b
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assert diff == expected
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def idfn(val):
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if isinstance(val, (datetime,)):
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# note this wouldn't show any hours/minutes/seconds
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return val.strftime("%Y%m%d")
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@pytest.mark.parametrize("a,b,expected", testdata, ids=idfn)
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def test_timedistance_v2(a, b, expected):
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diff = a - b
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assert diff == expected
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@pytest.mark.parametrize(
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"a,b,expected",
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[
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pytest.param(
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datetime(2001, 12, 12), datetime(2001, 12, 11), timedelta(1), id="forward"
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),
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pytest.param(
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datetime(2001, 12, 11), datetime(2001, 12, 12), timedelta(-1), id="backward"
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),
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],
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)
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def test_timedistance_v3(a, b, expected):
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diff = a - b
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assert diff == expected
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In ``test_timedistance_v0``, we let pytest generate the test IDs.
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In ``test_timedistance_v1``, we specified ``ids`` as a list of strings which were
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used as the test IDs. These are succinct, but can be a pain to maintain.
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In ``test_timedistance_v2``, we specified ``ids`` as a function that can generate a
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string representation to make part of the test ID. So our ``datetime`` values use the
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label generated by ``idfn``, but because we didn't generate a label for ``timedelta``
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objects, they are still using the default pytest representation:
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.. code-block:: pytest
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$ pytest test_time.py --collect-only
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=========================== test session starts ============================
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platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y
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cachedir: $PYTHON_PREFIX/.pytest_cache
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rootdir: $REGENDOC_TMPDIR
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collected 8 items
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<Module test_time.py>
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<Function test_timedistance_v0[a0-b0-expected0]>
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<Function test_timedistance_v0[a1-b1-expected1]>
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<Function test_timedistance_v1[forward]>
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<Function test_timedistance_v1[backward]>
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<Function test_timedistance_v2[20011212-20011211-expected0]>
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<Function test_timedistance_v2[20011211-20011212-expected1]>
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<Function test_timedistance_v3[forward]>
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<Function test_timedistance_v3[backward]>
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========================== no tests ran in 0.12s ===========================
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In ``test_timedistance_v3``, we used ``pytest.param`` to specify the test IDs
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together with the actual data, instead of listing them separately.
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A quick port of "testscenarios"
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------------------------------------
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.. _`test scenarios`: https://pypi.org/project/testscenarios/
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Here is a quick port to run tests configured with `test scenarios`_,
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an add-on from Robert Collins for the standard unittest framework. We
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only have to work a bit to construct the correct arguments for pytest's
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:py:func:`Metafunc.parametrize`:
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.. code-block:: python
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# content of test_scenarios.py
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def pytest_generate_tests(metafunc):
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idlist = []
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argvalues = []
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for scenario in metafunc.cls.scenarios:
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idlist.append(scenario[0])
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items = scenario[1].items()
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argnames = [x[0] for x in items]
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argvalues.append([x[1] for x in items])
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metafunc.parametrize(argnames, argvalues, ids=idlist, scope="class")
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scenario1 = ("basic", {"attribute": "value"})
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scenario2 = ("advanced", {"attribute": "value2"})
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class TestSampleWithScenarios:
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scenarios = [scenario1, scenario2]
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def test_demo1(self, attribute):
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assert isinstance(attribute, str)
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def test_demo2(self, attribute):
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assert isinstance(attribute, str)
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this is a fully self-contained example which you can run with:
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.. code-block:: pytest
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$ pytest test_scenarios.py
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=========================== test session starts ============================
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platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y
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cachedir: $PYTHON_PREFIX/.pytest_cache
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rootdir: $REGENDOC_TMPDIR
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collected 4 items
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test_scenarios.py .... [100%]
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============================ 4 passed in 0.12s =============================
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If you just collect tests you'll also nicely see 'advanced' and 'basic' as variants for the test function:
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.. code-block:: pytest
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$ pytest --collect-only test_scenarios.py
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=========================== test session starts ============================
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platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y
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cachedir: $PYTHON_PREFIX/.pytest_cache
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rootdir: $REGENDOC_TMPDIR
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collected 4 items
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<Module test_scenarios.py>
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<Class TestSampleWithScenarios>
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<Function test_demo1[basic]>
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<Function test_demo2[basic]>
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<Function test_demo1[advanced]>
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<Function test_demo2[advanced]>
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========================== no tests ran in 0.12s ===========================
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Note that we told ``metafunc.parametrize()`` that your scenario values
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should be considered class-scoped. With pytest-2.3 this leads to a
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resource-based ordering.
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Deferring the setup of parametrized resources
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---------------------------------------------------
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.. regendoc:wipe
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The parametrization of test functions happens at collection
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time. It is a good idea to setup expensive resources like DB
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connections or subprocess only when the actual test is run.
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Here is a simple example how you can achieve that. This test
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requires a ``db`` object fixture:
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.. code-block:: python
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# content of test_backends.py
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import pytest
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def test_db_initialized(db):
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# a dummy test
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if db.__class__.__name__ == "DB2":
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pytest.fail("deliberately failing for demo purposes")
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We can now add a test configuration that generates two invocations of
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the ``test_db_initialized`` function and also implements a factory that
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creates a database object for the actual test invocations:
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.. code-block:: python
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# content of conftest.py
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import pytest
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def pytest_generate_tests(metafunc):
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if "db" in metafunc.fixturenames:
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metafunc.parametrize("db", ["d1", "d2"], indirect=True)
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class DB1:
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"one database object"
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class DB2:
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"alternative database object"
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@pytest.fixture
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def db(request):
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if request.param == "d1":
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return DB1()
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elif request.param == "d2":
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return DB2()
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else:
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raise ValueError("invalid internal test config")
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Let's first see how it looks like at collection time:
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.. code-block:: pytest
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$ pytest test_backends.py --collect-only
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=========================== test session starts ============================
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platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y
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cachedir: $PYTHON_PREFIX/.pytest_cache
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rootdir: $REGENDOC_TMPDIR
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collected 2 items
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<Module test_backends.py>
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<Function test_db_initialized[d1]>
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<Function test_db_initialized[d2]>
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========================== no tests ran in 0.12s ===========================
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And then when we run the test:
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.. code-block:: pytest
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$ pytest -q test_backends.py
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.F [100%]
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================================= FAILURES =================================
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_________________________ test_db_initialized[d2] __________________________
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db = <conftest.DB2 object at 0xdeadbeef>
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def test_db_initialized(db):
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# a dummy test
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if db.__class__.__name__ == "DB2":
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> pytest.fail("deliberately failing for demo purposes")
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E Failed: deliberately failing for demo purposes
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test_backends.py:8: Failed
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1 failed, 1 passed in 0.12s
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The first invocation with ``db == "DB1"`` passed while the second with ``db == "DB2"`` failed. Our ``db`` fixture function has instantiated each of the DB values during the setup phase while the ``pytest_generate_tests`` generated two according calls to the ``test_db_initialized`` during the collection phase.
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.. regendoc:wipe
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Apply indirect on particular arguments
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---------------------------------------------------
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Very often parametrization uses more than one argument name. There is opportunity to apply ``indirect``
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parameter on particular arguments. It can be done by passing list or tuple of
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arguments' names to ``indirect``. In the example below there is a function ``test_indirect`` which uses
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two fixtures: ``x`` and ``y``. Here we give to indirect the list, which contains the name of the
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fixture ``x``. The indirect parameter will be applied to this argument only, and the value ``a``
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will be passed to respective fixture function:
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.. code-block:: python
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# content of test_indirect_list.py
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import pytest
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@pytest.fixture(scope="function")
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def x(request):
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return request.param * 3
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@pytest.fixture(scope="function")
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def y(request):
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return request.param * 2
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@pytest.mark.parametrize("x, y", [("a", "b")], indirect=["x"])
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def test_indirect(x, y):
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assert x == "aaa"
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assert y == "b"
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The result of this test will be successful:
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.. code-block:: pytest
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$ pytest test_indirect_list.py --collect-only
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=========================== test session starts ============================
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platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y
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cachedir: $PYTHON_PREFIX/.pytest_cache
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rootdir: $REGENDOC_TMPDIR
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collected 1 item
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<Module test_indirect_list.py>
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<Function test_indirect[a-b]>
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========================== no tests ran in 0.12s ===========================
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.. regendoc:wipe
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Note, that each argument in `parametrize` list should be explicitly declared in corresponding
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python test function or via `indirect`.
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Parametrizing test methods through per-class configuration
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--------------------------------------------------------------
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.. _`unittest parametrizer`: https://github.com/testing-cabal/unittest-ext/blob/master/params.py
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Here is an example ``pytest_generate_tests`` function implementing a
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parametrization scheme similar to Michael Foord's `unittest
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parametrizer`_ but in a lot less code:
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.. code-block:: python
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# content of ./test_parametrize.py
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import pytest
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def pytest_generate_tests(metafunc):
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# called once per each test function
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funcarglist = metafunc.cls.params[metafunc.function.__name__]
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argnames = sorted(funcarglist[0])
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metafunc.parametrize(
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argnames, [[funcargs[name] for name in argnames] for funcargs in funcarglist]
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)
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class TestClass:
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# a map specifying multiple argument sets for a test method
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params = {
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"test_equals": [dict(a=1, b=2), dict(a=3, b=3)],
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"test_zerodivision": [dict(a=1, b=0)],
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}
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def test_equals(self, a, b):
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assert a == b
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def test_zerodivision(self, a, b):
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with pytest.raises(ZeroDivisionError):
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a / b
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Our test generator looks up a class-level definition which specifies which
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argument sets to use for each test function. Let's run it:
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.. code-block:: pytest
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$ pytest -q
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F.. [100%]
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================================= FAILURES =================================
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________________________ TestClass.test_equals[1-2] ________________________
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self = <test_parametrize.TestClass object at 0xdeadbeef>, a = 1, b = 2
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def test_equals(self, a, b):
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> assert a == b
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E assert 1 == 2
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test_parametrize.py:21: AssertionError
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1 failed, 2 passed in 0.12s
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Indirect parametrization with multiple fixtures
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--------------------------------------------------------------
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Here is a stripped down real-life example of using parametrized
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testing for testing serialization of objects between different python
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interpreters. We define a ``test_basic_objects`` function which
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is to be run with different sets of arguments for its three arguments:
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* ``python1``: first python interpreter, run to pickle-dump an object to a file
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* ``python2``: second interpreter, run to pickle-load an object from a file
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* ``obj``: object to be dumped/loaded
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.. literalinclude:: multipython.py
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Running it results in some skips if we don't have all the python interpreters installed and otherwise runs all combinations (3 interpreters times 3 interpreters times 3 objects to serialize/deserialize):
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.. code-block:: pytest
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. $ pytest -rs -q multipython.py
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ssssssssssss...ssssssssssss [100%]
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========================= short test summary info ==========================
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SKIPPED [12] $REGENDOC_TMPDIR/CWD/multipython.py:29: 'python3.5' not found
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SKIPPED [12] $REGENDOC_TMPDIR/CWD/multipython.py:29: 'python3.7' not found
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3 passed, 24 skipped in 0.12s
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Indirect parametrization of optional implementations/imports
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--------------------------------------------------------------------
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If you want to compare the outcomes of several implementations of a given
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API, you can write test functions that receive the already imported implementations
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and get skipped in case the implementation is not importable/available. Let's
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say we have a "base" implementation and the other (possibly optimized ones)
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need to provide similar results:
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.. code-block:: python
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# content of conftest.py
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import pytest
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@pytest.fixture(scope="session")
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def basemod(request):
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return pytest.importorskip("base")
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@pytest.fixture(scope="session", params=["opt1", "opt2"])
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def optmod(request):
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return pytest.importorskip(request.param)
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And then a base implementation of a simple function:
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.. code-block:: python
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# content of base.py
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def func1():
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return 1
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And an optimized version:
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.. code-block:: python
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# content of opt1.py
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def func1():
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return 1.0001
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And finally a little test module:
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.. code-block:: python
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# content of test_module.py
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def test_func1(basemod, optmod):
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assert round(basemod.func1(), 3) == round(optmod.func1(), 3)
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If you run this with reporting for skips enabled:
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.. code-block:: pytest
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$ pytest -rs test_module.py
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=========================== test session starts ============================
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platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y
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cachedir: $PYTHON_PREFIX/.pytest_cache
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rootdir: $REGENDOC_TMPDIR
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collected 2 items
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|
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test_module.py .s [100%]
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|
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========================= short test summary info ==========================
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|
SKIPPED [1] $REGENDOC_TMPDIR/conftest.py:12: could not import 'opt2': No module named 'opt2'
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======================= 1 passed, 1 skipped in 0.12s =======================
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|
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|
You'll see that we don't have an ``opt2`` module and thus the second test run
|
|
of our ``test_func1`` was skipped. A few notes:
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|
|
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- the fixture functions in the ``conftest.py`` file are "session-scoped" because we
|
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don't need to import more than once
|
|
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- if you have multiple test functions and a skipped import, you will see
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the ``[1]`` count increasing in the report
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|
|
|
- you can put :ref:`@pytest.mark.parametrize <@pytest.mark.parametrize>` style
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parametrization on the test functions to parametrize input/output
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|
values as well.
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|
|
|
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|
Set marks or test ID for individual parametrized test
|
|
--------------------------------------------------------------------
|
|
|
|
Use ``pytest.param`` to apply marks or set test ID to individual parametrized test.
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|
For example:
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|
|
|
.. code-block:: python
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|
|
|
# content of test_pytest_param_example.py
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|
import pytest
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|
|
|
|
@pytest.mark.parametrize(
|
|
"test_input,expected",
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|
[
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|
("3+5", 8),
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|
pytest.param("1+7", 8, marks=pytest.mark.basic),
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|
pytest.param("2+4", 6, marks=pytest.mark.basic, id="basic_2+4"),
|
|
pytest.param(
|
|
"6*9", 42, marks=[pytest.mark.basic, pytest.mark.xfail], id="basic_6*9"
|
|
),
|
|
],
|
|
)
|
|
def test_eval(test_input, expected):
|
|
assert eval(test_input) == expected
|
|
|
|
In this example, we have 4 parametrized tests. Except for the first test,
|
|
we mark the rest three parametrized tests with the custom marker ``basic``,
|
|
and for the fourth test we also use the built-in mark ``xfail`` to indicate this
|
|
test is expected to fail. For explicitness, we set test ids for some tests.
|
|
|
|
Then run ``pytest`` with verbose mode and with only the ``basic`` marker:
|
|
|
|
.. code-block:: pytest
|
|
|
|
$ pytest -v -m basic
|
|
=========================== test session starts ============================
|
|
platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y -- $PYTHON_PREFIX/bin/python
|
|
cachedir: $PYTHON_PREFIX/.pytest_cache
|
|
rootdir: $REGENDOC_TMPDIR
|
|
collecting ... collected 17 items / 14 deselected / 3 selected
|
|
|
|
test_pytest_param_example.py::test_eval[1+7-8] PASSED [ 33%]
|
|
test_pytest_param_example.py::test_eval[basic_2+4] PASSED [ 66%]
|
|
test_pytest_param_example.py::test_eval[basic_6*9] XFAIL [100%]
|
|
|
|
=============== 2 passed, 14 deselected, 1 xfailed in 0.12s ================
|
|
|
|
As the result:
|
|
|
|
- Four tests were collected
|
|
- One test was deselected because it doesn't have the ``basic`` mark.
|
|
- Three tests with the ``basic`` mark was selected.
|
|
- The test ``test_eval[1+7-8]`` passed, but the name is autogenerated and confusing.
|
|
- The test ``test_eval[basic_2+4]`` passed.
|
|
- The test ``test_eval[basic_6*9]`` was expected to fail and did fail.
|
|
|
|
.. _`parametrizing_conditional_raising`:
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|
|
|
Parametrizing conditional raising
|
|
--------------------------------------------------------------------
|
|
|
|
Use :func:`pytest.raises` with the
|
|
:ref:`pytest.mark.parametrize ref` decorator to write parametrized tests
|
|
in which some tests raise exceptions and others do not.
|
|
|
|
It is helpful to define a no-op context manager ``does_not_raise`` to serve
|
|
as a complement to ``raises``. For example:
|
|
|
|
.. code-block:: python
|
|
|
|
from contextlib import contextmanager
|
|
import pytest
|
|
|
|
|
|
@contextmanager
|
|
def does_not_raise():
|
|
yield
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"example_input,expectation",
|
|
[
|
|
(3, does_not_raise()),
|
|
(2, does_not_raise()),
|
|
(1, does_not_raise()),
|
|
(0, pytest.raises(ZeroDivisionError)),
|
|
],
|
|
)
|
|
def test_division(example_input, expectation):
|
|
"""Test how much I know division."""
|
|
with expectation:
|
|
assert (6 / example_input) is not None
|
|
|
|
In the example above, the first three test cases should run unexceptionally,
|
|
while the fourth should raise ``ZeroDivisionError``.
|
|
|
|
If you're only supporting Python 3.7+, you can simply use ``nullcontext``
|
|
to define ``does_not_raise``:
|
|
|
|
.. code-block:: python
|
|
|
|
from contextlib import nullcontext as does_not_raise
|
|
|
|
Or, if you're supporting Python 3.3+ you can use:
|
|
|
|
.. code-block:: python
|
|
|
|
from contextlib import ExitStack as does_not_raise
|
|
|
|
Or, if desired, you can ``pip install contextlib2`` and use:
|
|
|
|
.. code-block:: python
|
|
|
|
from contextlib2 import nullcontext as does_not_raise
|