settings.rst

Settings

Hypothesis tries to have good defaults for its behaviour, but sometimes that's not enough and you need to tweak it.

The mechanism for doing this is the ~hypothesis.settings object. You can set up a @given <hypothesis.given> based test to use this using a settings decorator:

@given <hypothesis.given> invocation is as follows:

from hypothesis import given, settings


@given(integers())
@settings(max_examples=500)
def test_this_thoroughly(x):
    pass

This uses a ~hypothesis.settings object which causes the test to receive a much larger set of examples than normal.

This may be applied either before or after the given and the results are the same. The following is exactly equivalent:

from hypothesis import given, settings


@settings(max_examples=500)
@given(integers())
def test_this_thoroughly(x):
    pass

Available settings

hypothesis.settings

Controlling what runs

Hypothesis divides tests into logically distinct phases:

1. Running explicit examples provided with the @example decorator <providing-explicit-examples>.
2. Rerunning a selection of previously failing examples to reproduce a previously seen error.
3. Generating new examples.
4. Mutating examples for targeted property-based testing <targeted-search> (requires generate phase).
5. Attempting to shrink an example found in previous phases (other than phase 1 - explicit examples cannot be shrunk). This turns potentially large and complicated examples which may be hard to read into smaller and simpler ones.
6. Attempting to explain why your test failed (requires shrink phase).

Note

The explain phase has two parts, each of which is best-effort - if Hypothesis can't find a useful explanation, we'll just print the minimal failing example.

Following the first failure, Hypothesis will (usually <phases>) track which lines of code are always run on failing but never on passing inputs. This relies on python:sys.settrace, and is therefore automatically disabled on PyPy or if you are using coverage or a debugger. If there are no clearly suspicious lines of code, we refuse the temptation to guess <20>.

After shrinking to a minimal failing example, Hypothesis will try to find parts of the example -- e.g. separate args to @given() <hypothesis.given> -- which can vary freely without changing the result of that minimal failing example. If the automated experiments run without finding a passing variation, we leave a comment in the final report:

test_x_divided_by_y(
    x=0,  # or any other generated value
    y=0,
)

Just remember that the lack of an explanation sometimes just means that Hypothesis couldn't efficiently find one, not that no explanation (or simpler failing example) exists.

The phases setting provides you with fine grained control over which of these run, with each phase corresponding to a value on the ~hypothesis.Phase enum:

hypothesis.Phase

The phases argument accepts a collection with any subset of these. e.g. settings(phases=[Phase.generate, Phase.shrink]) will generate new examples and shrink them, but will not run explicit examples or reuse previous failures, while settings(phases=[Phase.explicit]) will only run the explicit examples.

Seeing intermediate result

To see what's going on while Hypothesis runs your tests, you can turn up the verbosity setting.

>>> from hypothesis import find, settings, Verbosity
>>> from hypothesis.strategies import lists, integers
>>> @given(lists(integers()))
... @settings(verbosity=Verbosity.verbose)
... def f(x):
...     assert not any(x)
... f()
Trying example: []
Falsifying example: [-1198601713, -67, 116, -29578]
Shrunk example to [-1198601713]
Shrunk example to [-1198601600]
Shrunk example to [-1191228800]
Shrunk example to [-8421504]
Shrunk example to [-32896]
Shrunk example to [-128]
Shrunk example to [64]
Shrunk example to [32]
Shrunk example to [16]
Shrunk example to [8]
Shrunk example to [4]
Shrunk example to [3]
Shrunk example to [2]
Shrunk example to [1]
[1]

The four levels are quiet, normal, verbose and debug. normal is the default, while in quiet mode Hypothesis will not print anything out, not even the final falsifying example. debug is basically verbose but a bit more so. You probably don't want it.

If you are using pytest, you may also need to disable output capturing for passing tests <pytest:how-to/capture-stdout-stderr>.

Building settings objects

Settings can be created by calling ~hypothesis.settings with any of the available settings values. Any absent ones will be set to defaults:

>>> from hypothesis import settings
>>> settings().max_examples
100
>>> settings(max_examples=10).max_examples
10

You can also pass a 'parent' settings object as the first argument, and any settings you do not specify as keyword arguments will be copied from the parent settings:

>>> parent = settings(max_examples=10)
>>> child = settings(parent, deadline=None)
>>> parent.max_examples == child.max_examples == 10
True
>>> parent.deadline
200
>>> child.deadline is None
True

Default settings

At any given point in your program there is a current default settings, available as settings.default. As well as being a settings object in its own right, all newly created settings objects which are not explicitly based off another settings are based off the default, so will inherit any values that are not explicitly set from it.

You can change the defaults by using profiles.

Settings profiles

Depending on your environment you may want different default settings. For example: during development you may want to lower the number of examples to speed up the tests. However, in a CI environment you may want more examples so you are more likely to find bugs.

Hypothesis allows you to define different settings profiles. These profiles can be loaded at any time.

hypothesis.settings.register_profile

hypothesis.settings.get_profile

hypothesis.settings.load_profile

Loading a profile changes the default settings but will not change the behaviour of tests that explicitly change the settings.

>>> from hypothesis import settings
>>> settings.register_profile("ci", max_examples=1000)
>>> settings().max_examples
100
>>> settings.load_profile("ci")
>>> settings().max_examples
1000

Instead of loading the profile and overriding the defaults you can retrieve profiles for specific tests.

>>> settings.get_profile("ci").max_examples
1000

Optionally, you may define the environment variable to load a profile for you. This is the suggested pattern for running your tests on CI. The code below should run in a conftest.py or any setup/initialization section of your test suite. If this variable is not defined the Hypothesis defined defaults will be loaded.

>>> import os
>>> from hypothesis import settings, Verbosity
>>> settings.register_profile("ci", max_examples=1000)
>>> settings.register_profile("dev", max_examples=10)
>>> settings.register_profile("debug", max_examples=10, verbosity=Verbosity.verbose)
>>> settings.load_profile(os.getenv("HYPOTHESIS_PROFILE", "default"))

If you are using the hypothesis pytest plugin and your profiles are registered by your conftest you can load one with the command line option --hypothesis-profile.

$ pytest tests --hypothesis-profile <profile-name>

Health checks

Hypothesis' health checks are designed to detect and warn you about performance problems where your tests are slow, inefficient, or generating very large examples.

If this is expected, e.g. when generating large arrays or dataframes, you can selectively disable them with the ~hypothesis.settings.suppress_health_check setting. The argument for this parameter is a list with elements drawn from any of the class-level attributes of the HealthCheck class. Using a value of list(HealthCheck) will disable all health checks.

hypothesis.HealthCheck