539 lines
19 KiB
ReStructuredText
539 lines
19 KiB
ReStructuredText
|
|
.. _paramexamples:
|
|
|
|
Parametrizing tests
|
|
=================================================
|
|
|
|
.. currentmodule:: _pytest.python
|
|
|
|
``pytest`` allows to easily parametrize test functions.
|
|
For basic docs, see :ref:`parametrize-basics`.
|
|
|
|
In the following we provide some examples using
|
|
the builtin mechanisms.
|
|
|
|
Generating parameters combinations, depending on command line
|
|
----------------------------------------------------------------------------
|
|
|
|
.. regendoc:wipe
|
|
|
|
Let's say we want to execute a test with different computation
|
|
parameters and the parameter range shall be determined by a command
|
|
line argument. Let's first write a simple (do-nothing) computation test::
|
|
|
|
# content of test_compute.py
|
|
|
|
def test_compute(param1):
|
|
assert param1 < 4
|
|
|
|
Now we add a test configuration like this::
|
|
|
|
# content of conftest.py
|
|
|
|
def pytest_addoption(parser):
|
|
parser.addoption("--all", action="store_true",
|
|
help="run all combinations")
|
|
|
|
def pytest_generate_tests(metafunc):
|
|
if 'param1' in metafunc.fixturenames:
|
|
if metafunc.config.getoption('all'):
|
|
end = 5
|
|
else:
|
|
end = 2
|
|
metafunc.parametrize("param1", range(end))
|
|
|
|
This means that we only run 2 tests if we do not pass ``--all``::
|
|
|
|
$ pytest -q test_compute.py
|
|
.. [100%]
|
|
2 passed in 0.12 seconds
|
|
|
|
We run only two computations, so we see two dots.
|
|
let's run the full monty::
|
|
|
|
$ pytest -q --all
|
|
....F [100%]
|
|
================================= FAILURES =================================
|
|
_____________________________ test_compute[4] ______________________________
|
|
|
|
param1 = 4
|
|
|
|
def test_compute(param1):
|
|
> assert param1 < 4
|
|
E assert 4 < 4
|
|
|
|
test_compute.py:3: AssertionError
|
|
1 failed, 4 passed in 0.12 seconds
|
|
|
|
As expected when running the full range of ``param1`` values
|
|
we'll get an error on the last one.
|
|
|
|
|
|
Different options for test IDs
|
|
------------------------------------
|
|
|
|
pytest will build a string that is the test ID for each set of values in a
|
|
parametrized test. These IDs can be used with ``-k`` to select specific cases
|
|
to run, and they will also identify the specific case when one is failing.
|
|
Running pytest with ``--collect-only`` will show the generated IDs.
|
|
|
|
Numbers, strings, booleans and None will have their usual string representation
|
|
used in the test ID. For other objects, pytest will make a string based on
|
|
the argument name::
|
|
|
|
# content of test_time.py
|
|
|
|
import pytest
|
|
|
|
from datetime import datetime, timedelta
|
|
|
|
testdata = [
|
|
(datetime(2001, 12, 12), datetime(2001, 12, 11), timedelta(1)),
|
|
(datetime(2001, 12, 11), datetime(2001, 12, 12), timedelta(-1)),
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize("a,b,expected", testdata)
|
|
def test_timedistance_v0(a, b, expected):
|
|
diff = a - b
|
|
assert diff == expected
|
|
|
|
|
|
@pytest.mark.parametrize("a,b,expected", testdata, ids=["forward", "backward"])
|
|
def test_timedistance_v1(a, b, expected):
|
|
diff = a - b
|
|
assert diff == expected
|
|
|
|
|
|
def idfn(val):
|
|
if isinstance(val, (datetime,)):
|
|
# note this wouldn't show any hours/minutes/seconds
|
|
return val.strftime('%Y%m%d')
|
|
|
|
|
|
@pytest.mark.parametrize("a,b,expected", testdata, ids=idfn)
|
|
def test_timedistance_v2(a, b, expected):
|
|
diff = a - b
|
|
assert diff == expected
|
|
|
|
@pytest.mark.parametrize("a,b,expected", [
|
|
pytest.param(datetime(2001, 12, 12), datetime(2001, 12, 11),
|
|
timedelta(1), id='forward'),
|
|
pytest.param(datetime(2001, 12, 11), datetime(2001, 12, 12),
|
|
timedelta(-1), id='backward'),
|
|
])
|
|
def test_timedistance_v3(a, b, expected):
|
|
diff = a - b
|
|
assert diff == expected
|
|
|
|
In ``test_timedistance_v0``, we let pytest generate the test IDs.
|
|
|
|
In ``test_timedistance_v1``, we specified ``ids`` as a list of strings which were
|
|
used as the test IDs. These are succinct, but can be a pain to maintain.
|
|
|
|
In ``test_timedistance_v2``, we specified ``ids`` as a function that can generate a
|
|
string representation to make part of the test ID. So our ``datetime`` values use the
|
|
label generated by ``idfn``, but because we didn't generate a label for ``timedelta``
|
|
objects, they are still using the default pytest representation::
|
|
|
|
|
|
$ pytest test_time.py --collect-only
|
|
=========================== test session starts ============================
|
|
platform linux -- Python 3.x.y, pytest-3.x.y, py-1.x.y, pluggy-0.x.y
|
|
rootdir: $REGENDOC_TMPDIR, inifile:
|
|
collected 8 items
|
|
<Module 'test_time.py'>
|
|
<Function 'test_timedistance_v0[a0-b0-expected0]'>
|
|
<Function 'test_timedistance_v0[a1-b1-expected1]'>
|
|
<Function 'test_timedistance_v1[forward]'>
|
|
<Function 'test_timedistance_v1[backward]'>
|
|
<Function 'test_timedistance_v2[20011212-20011211-expected0]'>
|
|
<Function 'test_timedistance_v2[20011211-20011212-expected1]'>
|
|
<Function 'test_timedistance_v3[forward]'>
|
|
<Function 'test_timedistance_v3[backward]'>
|
|
|
|
======================= no tests ran in 0.12 seconds =======================
|
|
|
|
In ``test_timedistance_v3``, we used ``pytest.param`` to specify the test IDs
|
|
together with the actual data, instead of listing them separately.
|
|
|
|
A quick port of "testscenarios"
|
|
------------------------------------
|
|
|
|
.. _`test scenarios`: https://pypi.org/project/testscenarios/
|
|
|
|
Here is a quick port to run tests configured with `test scenarios`_,
|
|
an add-on from Robert Collins for the standard unittest framework. We
|
|
only have to work a bit to construct the correct arguments for pytest's
|
|
:py:func:`Metafunc.parametrize`::
|
|
|
|
# content of test_scenarios.py
|
|
|
|
def pytest_generate_tests(metafunc):
|
|
idlist = []
|
|
argvalues = []
|
|
for scenario in metafunc.cls.scenarios:
|
|
idlist.append(scenario[0])
|
|
items = scenario[1].items()
|
|
argnames = [x[0] for x in items]
|
|
argvalues.append(([x[1] for x in items]))
|
|
metafunc.parametrize(argnames, argvalues, ids=idlist, scope="class")
|
|
|
|
scenario1 = ('basic', {'attribute': 'value'})
|
|
scenario2 = ('advanced', {'attribute': 'value2'})
|
|
|
|
class TestSampleWithScenarios(object):
|
|
scenarios = [scenario1, scenario2]
|
|
|
|
def test_demo1(self, attribute):
|
|
assert isinstance(attribute, str)
|
|
|
|
def test_demo2(self, attribute):
|
|
assert isinstance(attribute, str)
|
|
|
|
this is a fully self-contained example which you can run with::
|
|
|
|
$ pytest test_scenarios.py
|
|
=========================== test session starts ============================
|
|
platform linux -- Python 3.x.y, pytest-3.x.y, py-1.x.y, pluggy-0.x.y
|
|
rootdir: $REGENDOC_TMPDIR, inifile:
|
|
collected 4 items
|
|
|
|
test_scenarios.py .... [100%]
|
|
|
|
========================= 4 passed in 0.12 seconds =========================
|
|
|
|
If you just collect tests you'll also nicely see 'advanced' and 'basic' as variants for the test function::
|
|
|
|
|
|
$ pytest --collect-only test_scenarios.py
|
|
=========================== test session starts ============================
|
|
platform linux -- Python 3.x.y, pytest-3.x.y, py-1.x.y, pluggy-0.x.y
|
|
rootdir: $REGENDOC_TMPDIR, inifile:
|
|
collected 4 items
|
|
<Module 'test_scenarios.py'>
|
|
<Class 'TestSampleWithScenarios'>
|
|
<Instance '()'>
|
|
<Function 'test_demo1[basic]'>
|
|
<Function 'test_demo2[basic]'>
|
|
<Function 'test_demo1[advanced]'>
|
|
<Function 'test_demo2[advanced]'>
|
|
|
|
======================= no tests ran in 0.12 seconds =======================
|
|
|
|
Note that we told ``metafunc.parametrize()`` that your scenario values
|
|
should be considered class-scoped. With pytest-2.3 this leads to a
|
|
resource-based ordering.
|
|
|
|
Deferring the setup of parametrized resources
|
|
---------------------------------------------------
|
|
|
|
.. regendoc:wipe
|
|
|
|
The parametrization of test functions happens at collection
|
|
time. It is a good idea to setup expensive resources like DB
|
|
connections or subprocess only when the actual test is run.
|
|
Here is a simple example how you can achieve that, first
|
|
the actual test requiring a ``db`` object::
|
|
|
|
# content of test_backends.py
|
|
|
|
import pytest
|
|
def test_db_initialized(db):
|
|
# a dummy test
|
|
if db.__class__.__name__ == "DB2":
|
|
pytest.fail("deliberately failing for demo purposes")
|
|
|
|
We can now add a test configuration that generates two invocations of
|
|
the ``test_db_initialized`` function and also implements a factory that
|
|
creates a database object for the actual test invocations::
|
|
|
|
# content of conftest.py
|
|
import pytest
|
|
|
|
def pytest_generate_tests(metafunc):
|
|
if 'db' in metafunc.fixturenames:
|
|
metafunc.parametrize("db", ['d1', 'd2'], indirect=True)
|
|
|
|
class DB1(object):
|
|
"one database object"
|
|
class DB2(object):
|
|
"alternative database object"
|
|
|
|
@pytest.fixture
|
|
def db(request):
|
|
if request.param == "d1":
|
|
return DB1()
|
|
elif request.param == "d2":
|
|
return DB2()
|
|
else:
|
|
raise ValueError("invalid internal test config")
|
|
|
|
Let's first see how it looks like at collection time::
|
|
|
|
$ pytest test_backends.py --collect-only
|
|
=========================== test session starts ============================
|
|
platform linux -- Python 3.x.y, pytest-3.x.y, py-1.x.y, pluggy-0.x.y
|
|
rootdir: $REGENDOC_TMPDIR, inifile:
|
|
collected 2 items
|
|
<Module 'test_backends.py'>
|
|
<Function 'test_db_initialized[d1]'>
|
|
<Function 'test_db_initialized[d2]'>
|
|
|
|
======================= no tests ran in 0.12 seconds =======================
|
|
|
|
And then when we run the test::
|
|
|
|
$ pytest -q test_backends.py
|
|
.F [100%]
|
|
================================= FAILURES =================================
|
|
_________________________ test_db_initialized[d2] __________________________
|
|
|
|
db = <conftest.DB2 object at 0xdeadbeef>
|
|
|
|
def test_db_initialized(db):
|
|
# a dummy test
|
|
if db.__class__.__name__ == "DB2":
|
|
> pytest.fail("deliberately failing for demo purposes")
|
|
E Failed: deliberately failing for demo purposes
|
|
|
|
test_backends.py:6: Failed
|
|
1 failed, 1 passed in 0.12 seconds
|
|
|
|
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.
|
|
|
|
.. regendoc:wipe
|
|
|
|
Apply indirect on particular arguments
|
|
---------------------------------------------------
|
|
|
|
Very often parametrization uses more than one argument name. There is opportunity to apply ``indirect``
|
|
parameter on particular arguments. It can be done by passing list or tuple of
|
|
arguments' names to ``indirect``. In the example below there is a function ``test_indirect`` which uses
|
|
two fixtures: ``x`` and ``y``. Here we give to indirect the list, which contains the name of the
|
|
fixture ``x``. The indirect parameter will be applied to this argument only, and the value ``a``
|
|
will be passed to respective fixture function::
|
|
|
|
# content of test_indirect_list.py
|
|
|
|
import pytest
|
|
@pytest.fixture(scope='function')
|
|
def x(request):
|
|
return request.param * 3
|
|
|
|
@pytest.fixture(scope='function')
|
|
def y(request):
|
|
return request.param * 2
|
|
|
|
@pytest.mark.parametrize('x, y', [('a', 'b')], indirect=['x'])
|
|
def test_indirect(x,y):
|
|
assert x == 'aaa'
|
|
assert y == 'b'
|
|
|
|
The result of this test will be successful::
|
|
|
|
$ pytest test_indirect_list.py --collect-only
|
|
=========================== test session starts ============================
|
|
platform linux -- Python 3.x.y, pytest-3.x.y, py-1.x.y, pluggy-0.x.y
|
|
rootdir: $REGENDOC_TMPDIR, inifile:
|
|
collected 1 item
|
|
<Module 'test_indirect_list.py'>
|
|
<Function 'test_indirect[a-b]'>
|
|
|
|
======================= no tests ran in 0.12 seconds =======================
|
|
|
|
.. regendoc:wipe
|
|
|
|
Parametrizing test methods through per-class configuration
|
|
--------------------------------------------------------------
|
|
|
|
.. _`unittest parametrizer`: https://github.com/testing-cabal/unittest-ext/blob/master/params.py
|
|
|
|
|
|
Here is an example ``pytest_generate_tests`` function implementing a
|
|
parametrization scheme similar to Michael Foord's `unittest
|
|
parametrizer`_ but in a lot less code::
|
|
|
|
# content of ./test_parametrize.py
|
|
import pytest
|
|
|
|
def pytest_generate_tests(metafunc):
|
|
# called once per each test function
|
|
funcarglist = metafunc.cls.params[metafunc.function.__name__]
|
|
argnames = sorted(funcarglist[0])
|
|
metafunc.parametrize(argnames, [[funcargs[name] for name in argnames]
|
|
for funcargs in funcarglist])
|
|
|
|
class TestClass(object):
|
|
# a map specifying multiple argument sets for a test method
|
|
params = {
|
|
'test_equals': [dict(a=1, b=2), dict(a=3, b=3), ],
|
|
'test_zerodivision': [dict(a=1, b=0), ],
|
|
}
|
|
|
|
def test_equals(self, a, b):
|
|
assert a == b
|
|
|
|
def test_zerodivision(self, a, b):
|
|
pytest.raises(ZeroDivisionError, "a/b")
|
|
|
|
Our test generator looks up a class-level definition which specifies which
|
|
argument sets to use for each test function. Let's run it::
|
|
|
|
$ pytest -q
|
|
F.. [100%]
|
|
================================= FAILURES =================================
|
|
________________________ TestClass.test_equals[1-2] ________________________
|
|
|
|
self = <test_parametrize.TestClass object at 0xdeadbeef>, a = 1, b = 2
|
|
|
|
def test_equals(self, a, b):
|
|
> assert a == b
|
|
E assert 1 == 2
|
|
|
|
test_parametrize.py:18: AssertionError
|
|
1 failed, 2 passed in 0.12 seconds
|
|
|
|
Indirect parametrization with multiple fixtures
|
|
--------------------------------------------------------------
|
|
|
|
Here is a stripped down real-life example of using parametrized
|
|
testing for testing serialization of objects between different python
|
|
interpreters. We define a ``test_basic_objects`` function which
|
|
is to be run with different sets of arguments for its three arguments:
|
|
|
|
* ``python1``: first python interpreter, run to pickle-dump an object to a file
|
|
* ``python2``: second interpreter, run to pickle-load an object from a file
|
|
* ``obj``: object to be dumped/loaded
|
|
|
|
.. literalinclude:: multipython.py
|
|
|
|
Running it results in some skips if we don't have all the python interpreters installed and otherwise runs all combinations (5 interpreters times 5 interpreters times 3 objects to serialize/deserialize)::
|
|
|
|
. $ pytest -rs -q multipython.py
|
|
...sss...sssssssss...sss... [100%]
|
|
========================= short test summary info ==========================
|
|
SKIP [15] $REGENDOC_TMPDIR/CWD/multipython.py:30: 'python3.4' not found
|
|
12 passed, 15 skipped in 0.12 seconds
|
|
|
|
Indirect parametrization of optional implementations/imports
|
|
--------------------------------------------------------------------
|
|
|
|
If you want to compare the outcomes of several implementations of a given
|
|
API, you can write test functions that receive the already imported implementations
|
|
and get skipped in case the implementation is not importable/available. Let's
|
|
say we have a "base" implementation and the other (possibly optimized ones)
|
|
need to provide similar results::
|
|
|
|
# content of conftest.py
|
|
|
|
import pytest
|
|
|
|
@pytest.fixture(scope="session")
|
|
def basemod(request):
|
|
return pytest.importorskip("base")
|
|
|
|
@pytest.fixture(scope="session", params=["opt1", "opt2"])
|
|
def optmod(request):
|
|
return pytest.importorskip(request.param)
|
|
|
|
And then a base implementation of a simple function::
|
|
|
|
# content of base.py
|
|
def func1():
|
|
return 1
|
|
|
|
And an optimized version::
|
|
|
|
# content of opt1.py
|
|
def func1():
|
|
return 1.0001
|
|
|
|
And finally a little test module::
|
|
|
|
# content of test_module.py
|
|
|
|
def test_func1(basemod, optmod):
|
|
assert round(basemod.func1(), 3) == round(optmod.func1(), 3)
|
|
|
|
|
|
If you run this with reporting for skips enabled::
|
|
|
|
$ pytest -rs test_module.py
|
|
=========================== test session starts ============================
|
|
platform linux -- Python 3.x.y, pytest-3.x.y, py-1.x.y, pluggy-0.x.y
|
|
rootdir: $REGENDOC_TMPDIR, inifile:
|
|
collected 2 items
|
|
|
|
test_module.py .s [100%]
|
|
========================= short test summary info ==========================
|
|
SKIP [1] $REGENDOC_TMPDIR/conftest.py:11: could not import 'opt2'
|
|
|
|
=================== 1 passed, 1 skipped in 0.12 seconds ====================
|
|
|
|
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:
|
|
|
|
- the fixture functions in the ``conftest.py`` file are "session-scoped" because we
|
|
don't need to import more than once
|
|
|
|
- if you have multiple test functions and a skipped import, you will see
|
|
the ``[1]`` count increasing in the report
|
|
|
|
- you can put :ref:`@pytest.mark.parametrize <@pytest.mark.parametrize>` style
|
|
parametrization on the test functions to parametrize input/output
|
|
values as well.
|
|
|
|
|
|
Set marks or test ID for individual parametrized test
|
|
--------------------------------------------------------------------
|
|
|
|
Use ``pytest.param`` to apply marks or set test ID to individual parametrized test.
|
|
For example::
|
|
|
|
# content of test_pytest_param_example.py
|
|
import pytest
|
|
@pytest.mark.parametrize('test_input,expected', [
|
|
('3+5', 8),
|
|
pytest.param('1+7', 8,
|
|
marks=pytest.mark.basic),
|
|
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::
|
|
|
|
pytest -v -m basic
|
|
============================================ test session starts =============================================
|
|
platform linux -- Python 3.x.y, pytest-3.x.y, py-1.x.y, pluggy-0.x.y
|
|
rootdir: $REGENDOC_TMPDIR, inifile:
|
|
collected 4 items
|
|
|
|
test_pytest_param_example.py::test_eval[1+7-8] PASSED
|
|
test_pytest_param_example.py::test_eval[basic_2+4] PASSED
|
|
test_pytest_param_example.py::test_eval[basic_6*9] xfail
|
|
========================================== short test summary info ===========================================
|
|
XFAIL test_pytest_param_example.py::test_eval[basic_6*9]
|
|
|
|
============================================= 1 tests deselected =============================================
|
|
|
|
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.
|