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Details and advanced features

This is an account of slightly less common Hypothesis features that you don't need to get started but will nevertheless make your life easier.

Additional test output

Normally the output of a failing test will look something like:

Falsifying example: test_a_thing(x=1, y="foo")

With the repr of each keyword argument being printed.

Sometimes this isn't enough, either because you have values with a repr that isn't very descriptive or because you need to see the output of some intermediate steps of your test. That's where the note function comes in:

.. autofunction:: hypothesis.note

>>> from hypothesis import given, note, strategies as st
>>> @given(st.lists(st.integers()), st.randoms())
... def test_shuffle_is_noop(ls, r):
...     ls2 = list(ls)
...     r.shuffle(ls2)
...     note("Shuffle: %r" % (ls2))
...     assert ls == ls2
...
>>> try:
...     test_shuffle_is_noop()
... except AssertionError:
...     print('ls != ls2')
Falsifying example: test_shuffle_is_noop(ls=[0, 0, 1], r=RandomWithSeed(0))
Shuffle: [0, 1, 0]
ls != ls2

The note is printed in the final run of the test in order to include any additional information you might need in your test.

Test Statistics

If you are using :pypi:`pytest` you can see a number of statistics about the executed tests by passing the command line argument --hypothesis-show-statistics. This will include some general statistics about the test:

For example if you ran the following with --hypothesis-show-statistics:

from hypothesis import given, strategies as st

@given(st.integers())
def test_integers(i):
    pass

You would see:

test_integers:

  - 100 passing examples, 0 failing examples, 0 invalid examples
  - Typical runtimes: ~ 1ms
  - Fraction of time spent in data generation: ~ 12%
  - Stopped because settings.max_examples=100

The final "Stopped because" line is particularly important to note: It tells you the setting value that determined when the test should stop trying new examples. This can be useful for understanding the behaviour of your tests. Ideally you'd always want this to be :obj:`~hypothesis.settings.max_examples`.

In some cases (such as filtered and recursive strategies) you will see events mentioned which describe some aspect of the data generation:

from hypothesis import given, strategies as st

@given(st.integers().filter(lambda x: x % 2 == 0))
def test_even_integers(i):
    pass

You would see something like:

test_even_integers:

    - 100 passing examples, 0 failing examples, 36 invalid examples
    - Typical runtimes: 0-1 ms
    - Fraction of time spent in data generation: ~ 16%
    - Stopped because settings.max_examples=100
    - Events:
      * 80.88%, Retried draw from integers().filter(lambda x: <unknown>) to satisfy filter
      * 26.47%, Aborted test because unable to satisfy integers().filter(lambda x: <unknown>)

You can also mark custom events in a test using the event function:

.. autofunction:: hypothesis.event

from hypothesis import given, event, strategies as st

@given(st.integers().filter(lambda x: x % 2 == 0))
def test_even_integers(i):
    event("i mod 3 = %d" % (i % 3,))

You will then see output like:

test_even_integers:

  - 100 passing examples, 0 failing examples, 38 invalid examples
  - Typical runtimes: 0-1 ms
  - Fraction of time spent in data generation: ~ 16%
  - Stopped because settings.max_examples=100
  - Events:
    * 80.43%, Retried draw from integers().filter(lambda x: <unknown>) to satisfy filter
    * 31.88%, i mod 3 = 0
    * 27.54%, Aborted test because unable to satisfy integers().filter(lambda x: <unknown>)
    * 21.74%, i mod 3 = 1
    * 18.84%, i mod 3 = 2

Arguments to event can be any hashable type, but two events will be considered the same if they are the same when converted to a string with :obj:`python:str`.

Making assumptions

Sometimes Hypothesis doesn't give you exactly the right sort of data you want - it's mostly of the right shape, but some examples won't work and you don't want to care about them. You can just ignore these by aborting the test early, but this runs the risk of accidentally testing a lot less than you think you are. Also it would be nice to spend less time on bad examples - if you're running 100 examples per test (the default) and it turns out 70 of those examples don't match your needs, that's a lot of wasted time.

.. autofunction:: hypothesis.assume

For example suppose you had the following test:

@given(floats())
def test_negation_is_self_inverse(x):
    assert x == -(-x)

Running this gives us:

Falsifying example: test_negation_is_self_inverse(x=float('nan'))
AssertionError

This is annoying. We know about NaN and don't really care about it, but as soon as Hypothesis finds a NaN example it will get distracted by that and tell us about it. Also the test will fail and we want it to pass.

So lets block off this particular example:

from math import isnan

@given(floats())
def test_negation_is_self_inverse_for_non_nan(x):
    assume(not isnan(x))
    assert x == -(-x)

And this passes without a problem.

In order to avoid the easy trap where you assume a lot more than you intended, Hypothesis will fail a test when it can't find enough examples passing the assumption.

If we'd written:

@given(floats())
def test_negation_is_self_inverse_for_non_nan(x):
    assume(False)
    assert x == -(-x)

Then on running we'd have got the exception:

Unsatisfiable: Unable to satisfy assumptions of hypothesis test_negation_is_self_inverse_for_non_nan. Only 0 examples considered satisfied assumptions

How good is assume?

Hypothesis has an adaptive exploration strategy to try to avoid things which falsify assumptions, which should generally result in it still being able to find examples in hard to find situations.

Suppose we had the following:

@given(lists(integers()))
def test_sum_is_positive(xs):
  assert sum(xs) > 0

Unsurprisingly this fails and gives the falsifying example [].

Adding assume(xs) to this removes the trivial empty example and gives us [0].

Adding assume(all(x > 0 for x in xs)) and it passes: the sum of a list of positive integers is positive.

The reason that this should be surprising is not that it doesn't find a counter-example, but that it finds enough examples at all.

In order to make sure something interesting is happening, suppose we wanted to try this for long lists. e.g. suppose we added an assume(len(xs) > 10) to it. This should basically never find an example: a naive strategy would find fewer than one in a thousand examples, because if each element of the list is negative with probability one-half, you'd have to have ten of these go the right way by chance. In the default configuration Hypothesis gives up long before it's tried 1000 examples (by default it tries 200).

Here's what happens if we try to run this:

@given(lists(integers()))
def test_sum_is_positive(xs):
    assume(len(xs) > 10)
    assume(all(x > 0 for x in xs))
    print(xs)
    assert sum(xs) > 0

In: test_sum_is_positive()
[17, 12, 7, 13, 11, 3, 6, 9, 8, 11, 47, 27, 1, 31, 1]
[6, 2, 29, 30, 25, 34, 19, 15, 50, 16, 10, 3, 16]
[25, 17, 9, 19, 15, 2, 2, 4, 22, 10, 10, 27, 3, 1, 14, 17, 13, 8, 16, 9, 2...
[17, 65, 78, 1, 8, 29, 2, 79, 28, 18, 39]
[13, 26, 8, 3, 4, 76, 6, 14, 20, 27, 21, 32, 14, 42, 9, 24, 33, 9, 5, 15, ...
[2, 1, 2, 2, 3, 10, 12, 11, 21, 11, 1, 16]

As you can see, Hypothesis doesn't find many examples here, but it finds some - enough to keep it happy.

In general if you can shape your strategies better to your tests you should - for example :py:func:`integers(1, 1000) <hypothesis.strategies.integers>` is a lot better than assume(1 <= x <= 1000), but assume will take you a long way if you can't.

Defining strategies

The type of object that is used to explore the examples given to your test function is called a :class:`~hypothesis.strategies.SearchStrategy`. These are created using the functions exposed in the :mod:`hypothesis.strategies` module.

Many of these strategies expose a variety of arguments you can use to customize generation. For example for integers you can specify min and max values of integers you want. If you want to see exactly what a strategy produces you can ask for an example:

>>> integers(min_value=0, max_value=10).example()
1

Many strategies are built out of other strategies. For example, if you want to define a tuple you need to say what goes in each element:

>>> from hypothesis.strategies import tuples
>>> tuples(integers(), integers()).example()
(-24597, 12566)

Further details are :doc:`available in a separate document <data>`.

The gory details of given parameters

.. autofunction:: hypothesis.given

The :func:`@given <hypothesis.given>` decorator may be used to specify which arguments of a function should be parametrized over. You can use either positional or keyword arguments, but not a mixture of both.

For example all of the following are valid uses:

@given(integers(), integers())
def a(x, y):
  pass

@given(integers())
def b(x, y):
  pass

@given(y=integers())
def c(x, y):
  pass

@given(x=integers())
def d(x, y):
  pass

@given(x=integers(), y=integers())
def e(x, **kwargs):
  pass

@given(x=integers(), y=integers())
def f(x, *args, **kwargs):
  pass


class SomeTest(TestCase):
    @given(integers())
    def test_a_thing(self, x):
        pass

The following are not:

@given(integers(), integers(), integers())
def g(x, y):
    pass

@given(integers())
def h(x, *args):
    pass

@given(integers(), x=integers())
def i(x, y):
    pass

@given()
def j(x, y):
    pass

The rules for determining what are valid uses of given are as follows:

  1. You may pass any keyword argument to given.
  2. Positional arguments to given are equivalent to the rightmost named arguments for the test function.
  3. Positional arguments may not be used if the underlying test function has varargs, arbitrary keywords, or keyword-only arguments.
  4. Functions tested with given may not have any defaults.

The reason for the "rightmost named arguments" behaviour is so that using :func:`@given <hypothesis.given>` with instance methods works: self will be passed to the function as normal and not be parametrized over.

The function returned by given has all the same arguments as the original test, minus those that are filled in by :func:`@given <hypothesis.given>`. Check :ref:`the notes on framework compatibility <framework-compatibility>` to see how this affects other testing libraries you may be using.

Custom function execution

Hypothesis provides you with a hook that lets you control how it runs examples.

This lets you do things like set up and tear down around each example, run examples in a subprocess, transform coroutine tests into normal tests, etc. For example, :class:`~hypothesis.extra.django.TransactionTestCase` in the Django extra runs each example in a separate database transaction.

The way this works is by introducing the concept of an executor. An executor is essentially a function that takes a block of code and run it. The default executor is:

def default_executor(function):
    return function()

You define executors by defining a method execute_example on a class. Any test methods on that class with :func:`@given <hypothesis.given>` used on them will use self.execute_example as an executor with which to run tests. For example, the following executor runs all its code twice:

from unittest import TestCase

class TestTryReallyHard(TestCase):
    @given(integers())
    def test_something(self, i):
        perform_some_unreliable_operation(i)

    def execute_example(self, f):
        f()
        return f()

Note: The functions you use in map, etc. will run inside the executor. i.e. they will not be called until you invoke the function passed to execute_example.

An executor must be able to handle being passed a function which returns None, otherwise it won't be able to run normal test cases. So for example the following executor is invalid:

from unittest import TestCase

class TestRunTwice(TestCase):
    def execute_example(self, f):
        return f()()

and should be rewritten as:

from unittest import TestCase

class TestRunTwice(TestCase):
    def execute_example(self, f):
        result = f()
        if callable(result):
            result = result()
        return result

An alternative hook is provided for use by test runner extensions such as :pypi:`pytest-trio`, which cannot use the execute_example method. This is not recommended for end-users - it is better to write a complete test function directly, perhaps by using a decorator to perform the same transformation before applying :func:`@given <hypothesis.given>`.

@given(x=integers())
@pytest.mark.trio
async def test(x):
    ...
# Illustrative code, inside the pytest-trio plugin
test.hypothesis.inner_test = lambda x: trio.run(test, x)

For authors of test runners however, assigning to the inner_test attribute of the hypothesis attribute of the test will replace the interior test.

Note

The new inner_test must accept and pass through all the *args and **kwargs expected by the original test.

If the end user has also specified a custom executor using the execute_example method, it - and all other execution-time logic - will be applied to the new inner test assigned by the test runner.

Using Hypothesis to find values

You can use Hypothesis's data exploration features to find values satisfying some predicate. This is generally useful for exploring custom strategies defined with :func:`@composite <hypothesis.strategies.composite>`, or experimenting with conditions for filtering data.

.. autofunction:: hypothesis.find

>>> from hypothesis import find
>>> from hypothesis.strategies import sets, lists, integers
>>> find(lists(integers()), lambda x: sum(x) >= 10)
[10]
>>> find(lists(integers()), lambda x: sum(x) >= 10 and len(x) >= 3)
[0, 0, 10]
>>> find(sets(integers()), lambda x: sum(x) >= 10 and len(x) >= 3)
{0, 1, 9}

The first argument to :func:`~hypothesis.find` describes data in the usual way for an argument to :func:`~hypothesis.given`, and supports :doc:`all the same data types <data>`. The second is a predicate it must satisfy.

Of course not all conditions are satisfiable. If you ask Hypothesis for an example to a condition that is always false it will raise an error:

>>> find(integers(), lambda x: False)
Traceback (most recent call last):
    ...
hypothesis.errors.NoSuchExample: No examples of condition lambda x: <unknown>

(The lambda x: unknown is because Hypothesis can't retrieve the source code of lambdas from the interactive python console. It gives a better error message most of the time which contains the actual condition)

Inferred Strategies

In some cases, Hypothesis can work out what to do when you omit arguments. This is based on introspection, not magic, and therefore has well-defined limits.

:func:`~hypothesis.strategies.builds` will check the signature of the target (using :func:`~python:inspect.getfullargspec`). If there are required arguments with type annotations and no strategy was passed to :func:`~hypothesis.strategies.builds`, :func:`~hypothesis.strategies.from_type` is used to fill them in. You can also pass the special value :const:`hypothesis.infer` as a keyword argument, to force this inference for arguments with a default value.

>>> def func(a: int, b: str):
...     return [a, b]
>>> builds(func).example()
[-6993, '']
.. data:: hypothesis.infer

:func:`@given <hypothesis.given>` does not perform any implicit inference for required arguments, as this would break compatibility with pytest fixtures. :const:`~hypothesis.infer` can be used as a keyword argument to explicitly fill in an argument from its type annotation.

@given(a=infer)
def test(a: int): pass
# is equivalent to
@given(a=integers())
def test(a): pass

Limitations

PEP 3107 type annotations are not supported on Python 2, and Hypothesis does not inspect PEP 484 type comments at runtime. While :func:`~hypothesis.strategies.from_type` will work as usual, inference in :func:`~hypothesis.strategies.builds` and :func:`@given <hypothesis.given>` will only work if you manually create the __annotations__ attribute (e.g. by using @annotations(...) and @returns(...) decorators). The :mod:`python:typing` module is fully supported on Python 2 if you have the backport installed.

The :mod:`python:typing` module is provisional and has a number of internal changes between Python 3.5.0 and 3.6.1, including at minor versions. These are all supported on a best-effort basis, but you may encounter problems with an old version of the module. Please report them to us, and consider updating to a newer version of Python as a workaround.

Type Annotations in Hypothesis

If you install Hypothesis and use :pypi:`mypy` 0.590+, or another PEP 561-compatible tool, the type checker should automatically pick up our type hints.

Note

Hypothesis' type hints may make breaking changes between minor releases.

Upstream tools and conventions about type hints remain in flux - for example the :mod:`python:typing` module itself is provisional, and Mypy has not yet reached version 1.0 - and we plan to support the latest version of this ecosystem, as well as older versions where practical.

We may also find more precise ways to describe the type of various interfaces, or change their type and runtime behaviour togther in a way which is otherwise backwards-compatible. We often omit type hints for deprecated features or arguments, as an additional form of warning.

There are known issues inferring the type of examples generated by :func:`~hypothesis.strategies.deferred`, :func:`~hypothesis.strategies.recursive`, :func:`~hypothesis.strategies.one_of`, :func:`~hypothesis.strategies.dictionaries`, and :func:`~hypothesis.strategies.fixed_dictionaries`. We will fix these, and require correspondingly newer versions of Mypy for type hinting, as the ecosystem improves.

Writing downstream type hints

Projects that :doc:`provide Hypothesis strategies <strategies>` and use type hints may wish to annotate their strategies too. This is a supported use-case, again on a best-effort provisional basis. For example:

def foo_strategy() -> SearchStrategy[Foo]: ...

:class:`~hypothesis.strategies.SearchStrategy` is the type of all strategy objects. It is a generic type, and covariant in the type of the examples it creates. For example:

  • integers() is of type SearchStrategy[int].
  • lists(integers()) is of type SearchStrategy[List[int]].
  • SearchStrategy[Dog] is a subtype of SearchStrategy[Animal] if Dog is a subtype of Animal (as seems likely).

Warning

:class:`~hypothesis.strategies.SearchStrategy` should only be used in type hints. Please do not inherit from, compare to, or otherwise use it in any way outside of type hints. The only supported way to construct objects of this type is to use the functions provided by the :mod:`hypothesis.strategies` module!

The Hypothesis pytest Plugin

Hypothesis includes a tiny plugin to improve integration with :pypi:`pytest`, which is activated by default (but does not affect other test runners). It aims to improve the integration between Hypothesis and Pytest by providing extra information and convenient access to config options.

Finally, all tests that are defined with Hypothesis automatically have @pytest.mark.hypothesis applied to them. See :ref:`here for information on working with markers <pytest:mark examples>`.

Note

Pytest will load the plugin automatically if Hypothesis is installed. You don't need to do anything at all to use it.