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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 settings object. You can set up a @given based test to use this using a settings decorator:
@given invocation is as follows:
This uses a 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:
Available settings ¶
A settings object configures options including verbosity, runtime controls, persistence, determinism, and more.
Default values are picked up from the settings.default object and changes made there will be picked up in newly created settings.
EXPERIMENTAL AND UNSTABLE - see Alternative backends for Hypothesis . The importable name of a backend which Hypothesis should use to generate primitive types. We aim to support heuristic-random, solver-based, and fuzzing-based backends.
default value: (dynamically calculated)
An instance of ExampleDatabase that will be used to save examples to and load previous examples from. May be None in which case no storage will be used.
See the example database documentation for a list of built-in example database implementations, and how to define custom implementations.
If set, a duration (as timedelta, or integer or float number of milliseconds) that each individual example (i.e. each time your test function is called, not the whole decorated test) within a test is not allowed to exceed. Tests which take longer than that may be converted into errors (but will not necessarily be if close to the deadline, to allow some variability in test run time).
Set this to None to disable this behaviour entirely.
default value: timedelta(milliseconds=200)
If True, seed Hypothesis’ random number generator using a hash of the test function, so that every run will test the same set of examples until you update Hypothesis, Python, or the test function.
This allows you to check for regressions and look for bugs using separate settings profiles - for example running quick deterministic tests on every commit, and a longer non-deterministic nightly testing run.
default value: False
Once this many satisfying examples have been considered without finding any counter-example, Hypothesis will stop looking.
Note that we might call your test function fewer times if we find a bug early or can tell that we’ve exhausted the search space; or more if we discard some examples due to use of .filter(), assume(), or a few other things that can prevent the test case from completing successfully.
The default value is chosen to suit a workflow where the test will be part of a suite that is regularly executed locally or on a CI server, balancing total running time against the chance of missing a bug.
If you are writing one-off tests, running tens of thousands of examples is quite reasonable as Hypothesis may miss uncommon bugs with default settings. For very complex code, we have observed Hypothesis finding novel bugs after several million examples while testing SymPy . If you are running more than 100k examples for a test, consider using our integration for coverage-guided fuzzing - it really shines when given minutes or hours to run.
default value: 100
Control which phases should be run. See the full documentation for more details
default value: (Phase.explicit, Phase.reuse, Phase.generate, Phase.target, Phase.shrink, Phase.explain)
If set to True , Hypothesis will print code for failing examples that can be used with @reproduce_failure to reproduce the failing example. The default is True if the CI or TF_BUILD env vars are set, False otherwise.
Because Hypothesis runs the test many times, it can sometimes find multiple bugs in a single run. Reporting all of them at once is usually very useful, but replacing the exceptions can occasionally clash with debuggers. If disabled, only the exception with the smallest minimal example is raised.
default value: True
Number of steps to run a stateful program for before giving up on it breaking.
default value: 50
A list of HealthCheck items to disable.
default value: ()
Control the verbosity level of Hypothesis messages
default value: Verbosity.normal
Controlling what runs ¶
Hypothesis divides tests into logically distinct phases:
Running explicit examples provided with the @example decorator .
Rerunning a selection of previously failing examples to reproduce a previously seen error.
Generating new examples.
Mutating examples for targeted property-based testing (requires generate phase).
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.
Attempting to explain why your test failed (requires shrink phase).
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 ) track which lines of code are always run on failing but never on passing inputs. This relies on 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 .
After shrinking to a minimal failing example, Hypothesis will try to find parts of the example – e.g. separate args to @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:
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 Phase enum:
An enumeration.
controls whether explicit examples are run.
controls whether previous examples will be reused.
controls whether new examples will be generated.
controls whether examples will be mutated for targeting.
controls whether examples will be shrunk.
controls whether Hypothesis attempts to explain test failures.
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.
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 .
Building settings objects ¶
Settings can be created by calling settings with any of the available settings values. Any absent ones will be set to defaults:
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:
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.
Registers a collection of values to be used as a settings profile.
Settings profiles can be loaded by name - for example, you might create a ‘fast’ profile which runs fewer examples, keep the ‘default’ profile, and create a ‘ci’ profile that increases the number of examples and uses a different database to store failures.
The arguments to this method are exactly as for settings : optional parent settings, and keyword arguments for each setting that will be set differently to parent (or settings.default, if parent is None).
Return the profile with the given name.
Loads in the settings defined in the profile provided.
If the profile does not exist, InvalidArgument will be raised. Any setting not defined in the profile will be the library defined default for that setting.
Loading a profile changes the default settings but will not change the behaviour of tests that explicitly change the settings.
Instead of loading the profile and overriding the defaults you can retrieve profiles for specific tests.
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.
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 .
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 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.
Arguments for suppress_health_check .
Each member of this enum is a type of health check to suppress.
Checks if too many examples are aborted for being too large.
This is measured by the number of random choices that Hypothesis makes in order to generate something, not the size of the generated object. For example, choosing a 100MB object from a predefined list would take only a few bits, while generating 10KB of JSON from scratch might trigger this health check.
Check for when the test is filtering out too many examples, either through use of assume() or filter() , or occasionally for Hypothesis internal reasons.
Check for when your data generation is extremely slow and likely to hurt testing.
Deprecated; we always error if a test returns a non-None value.
Checks if the natural example to shrink towards is very large.
Deprecated; we always error if @given is applied to a method defined by unittest.TestCase (i.e. not a test).
Checks if @given has been applied to a test with a pytest function-scoped fixture. Function-scoped fixtures run once for the whole function, not once per example, and this is usually not what you want.
Because of this limitation, tests that need to set up or reset state for every example need to do so manually within the test itself, typically using an appropriate context manager.
Suppress this health check only in the rare case that you are using a function-scoped fixture that does not need to be reset between individual examples, but for some reason you cannot use a wider fixture scope (e.g. session scope, module scope, class scope).
This check requires the Hypothesis pytest plugin , which is enabled by default when running Hypothesis inside pytest.
Checks if @given has been applied to a test which is executed by different executors . If your test function is defined as a method on a class, that class will be your executor, and subclasses executing an inherited test is a common way for things to go wrong.
The correct fix is often to bring the executor instance under the control of hypothesis by explicit parametrization over, or sampling from, subclasses, or to refactor so that @given is specified on leaf subclasses.
Blog Technical posts
Data-driven testing with python.
Pay attention to zeros. If there is a zero, someone will divide by it.
Writing code, a good test coverage is an essential component, both for the reliability of the code and for the peace of mind of the developer.
There are tools (for example Nose) to measure the coverage of the codebase by checking the number of lines that are run during the execution of tests. An excellent coverage of the lines of code, however, does not necessarily imply an equally good coverage of the functionality of the code: the same statement can work correctly with certain data and fail with other equally legitimate ones. Some values also lend themselves more than others to generate errors: edge cases are the limit values of an interval, the index of the last iteration of a cycle, characters encoded in unexpected way, zeros, and so on.
In order to have an effective coverage of this type of errors, it can be easy to find yourself having to replicate entire blocks of code in tests, varying only minimal parts of them.
In this article, we will look at some of the tools offered by the Python ecosystem to manage this need elegantly (and “DRY”).
py.test parametrize
Pytest is a valid alternative to Unittest at a distance of a pip install . The two main innovations introduced in Pytest are the fixture and the parametrize decorator. The former is used to manage the setup of a test in a more granular way than the classic setUp() method. In this blog post, however, we are mainly interested in the parametrize decorator, which allows us to take an abstraction step in the test-case writing, dividing the test logic from the data to be input. We can then verify the correct functioning of the code with different edge cases, while avoiding the duplication of logic.
In the example, test_func will be performed twice, the first with value_A = 'first case', value_B = 1 and the second with value_A = 'second case', value_B = 2 .
During execution of the tests, the various parameters will be considered as independent test-cases and, in the event of failure, an identifier containing the data provided allows the developer to quickly trace the problematic case.
Faker provides methods to spontaneously create plausible data for our tests.
The data is generated by Providers included in the library (a complete list in the documentation), but it is also possible to create custom ones.
then usable by adding them to the global object the library is based on:
To understand certain cases where Faker can come in handy, let’s suppose for example that you want to perform tests to verify the correct creation of users into a database.
In this case, one possibility would be to recreate the database each time the test suite is run. However, creating a database is usually an operation that takes time, so it would be preferable to create it only the first time, perhaps using a dedicated command line option. The problem here is that, if we use hardcoded data in the testcase and if there is some kind of constraint on the users (for example, the unique email), the test would fail if run twice on the same database. With Faker we can easily avoid these conflicts because instead of the explicit data we have a function call that returns different data each time.
In this case, however, we renounce the reproducibility of the test: as the values of Faker are chosen in a random manner, a value that shows an error in the code could be randomly provided or not, so the execution of the test would generate different results in an unpredictable way.
Hypothesis is a data generation engine. The programmer, in this case, establishes the criteria with which the data must be generated and the library deals with generating examples (the terminology used in this library is inspired by the scientific world. The data generated by Hypothesis are called “examples”. We will also see other keywords such as “given”, “assume”… that respect the given criteria).
For example, if we want to test a function that takes integers, it will be sufficient to apply the given decorator to the test and to pass to it the integers strategy . In the documentation you will find all the strategies included in the library.
The test test_my_function takes two parameters in input, value_A and value_B . Hypothesis, through the given , decorator, fills these parameters with valid data, according to the specified strategy.
The main advantage over Faker is that the test will be run numerous times, with combinations of values value_A and value_B that are different each time. Hypothesis is also designed to look for edge cases that could hide errors. In our example, we have not defined any minor or major limit for the integers to be generated, so it is reasonable to expect that among the examples generated we will find, in addition to the simplest cases, the zero and values high enough (in absolute value) to generate integer overflow in some representations.
These are some examples generated by the text strategy:
(yes, most of these characters don’t even display in my browser)
Delegating to an external library the task of imagining possible limit cases that could put our code in difficulty is a great way to find possible errors that were not thought of and at the same time to maintain the code of the lean test.
Note that the number of test runs is not at the discretion of the programmer. In particular, through the settings decorator it is possible to set a maximum limit of examples to be generated
but this limit could still be exceeded if the test fails. This behaviour is due to another feature of Hypothesis: in case of failure, a test is repeated with increasingly elementary examples, in order to recreate (and provide in the report) the simplest example that guarantees a code failure.
In this case, for example, Hypothesis manages to find the limit for which the code actually fails:
A slightly more realistic example can be this:
Hypothesis stores in its cache the values obtained from the “falsification” process and provides them as the very first examples in the subsequent executions of the test, to allow the developer to immediately verify whether a previously revealed bug has been solved or not. We therefore have the reproducibility of the test for the examples that caused failures. To formalise this behaviour and find it even in a non-local environment, like a continuous integration server, we can specify with the decorator examples a number of examples that will always be executed before those that are randomly generated.
example is also an excellent “bookmark” for those who will read the code in the future, as it highlights possible misleading cases that could be missed at first sight.
Hypothesis: creating personalised strategies
All this is very useful, but often in our tests we need more complex structures than a simple string. Hypothesis involves the use of certain tools to generate complex data at will.
To start, the data output from a strategy can be passed to a map or from a filter .
Another possibility is to link multiple strategies, using flatmap .
In the example the first call to st.integers determines the length of the lists generated by st.lists and places a maximum limit of 10 elements for them, excluding however lists with a length equal to 5 elements.
For more complex operations, we can instead use the strategies.composite decorator, which allows us to obtain data from existing strategies, to modify them and to assemble them in a new strategy to be used in tests or as a brick for another custom strategy.
For example, to generate a valid payload for a web application, we could write something like the following code.
Suppose the payloads we want to generate include a number of mandatory and other optional fields. We then construct a payloads strategy, which first extracts the values for the mandatory fields, inserts them into a dictionary and, in a second phase, enriches this dictionary with a subset of the optional fields.
In the example we also wanted to include assume , which provides an additional rule in data creation and can be very useful.
All that remains is for us to define subdictionaries : a utility function, usable both as a stand-alone strategy and as a component for other customised strategies.
Our subdictionaries is little more than a call to random.sample() , but using the randoms strategy we get that Hypothesis can handle the random seed and thus treat the personalised strategy exactly like those of the library, during the process of “falsification” of the failed test-cases.
In both functions a draw argument is taken in input, which is managed entirely by the given decorator. The use of the payload strategy will therefore be of this type:
The creation of customised strategies lends itself particularly well to testing the correct behaviour of the application, while to verify the behaviour of our code in the case of specific failures it could become overly burdensome. We can however reuse the work performed to write the custom strategy and to alter the data provided by Hypothesis such as to cause the failures we want to verify.
It is possible that, as the complexity and the nesting of the strategies grow, the data generation may become slower, to the point of causing a Hypothesis inner health check to fail:
However, if the complexity achieved is necessary for this purpose, we can suppress the control in question for those single tests that would risk random failures, by meddling with the settings decorator:
These are just some of the tools available for data-driven testing in Python, being a constantly evolving environment. Of pytest.parametrize we can state that it is a tool to bear in mind when writing tests, because essentially it helps us to obtain a more elegant code.
Faker is an interesting possibility, it can be used to see scrolling data in our tests, but it doesn’t add much, while Hypothesis is undoubtedly a more powerful and mature library. It must be said that writing strategies for Hypothesis are an activity that takes time, especially when the data to be generated consists of several nested parts; but all the tools needed to do it are available. Hypothesis is perhaps not suitable for a unit-test written quickly during the drafting of the code, but it is definitely useful for an in-depth analysis of its own sources. As often happens in Test Driven Development , the design of tests helps to write better quality code immediately: Hypothesis encourages the developer to evaluate those borderline cases that sometimes end up, instead, being omitted.
HealthCheck/Unsatisfiable errors with newer version
Hugh Hopper
Zac Hatfield Dodds
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FailedHealthCheck: Data generation is extremely slow: Only produced 6 valid examples in 1.09 seconds (0 invalid ones and 2 exceeded maximum size). Try decreasing size of the data you're generating (with e.g.max_size or max_leaves parameters).
But starting from 400 it generates errors like: hypothesis.errors.FailedHealthCheck: Data generation is extremely slow: Only produced 7 valid examples in 1.20 seconds (0 invalid ones and 2 exceeded maximum size). Try decreasing size of the data you're generating (with e.g.max_size or max_leaves parameters).
I have a recursive composite strategy that generates a small amount of data for each example, yet generates examples extremely slowly. I'm not sure why as no example is being rejected or filtered. Also as I've worked with this strategy, ...
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 suppress_health_check setting. The argument for this parameter is a ...
E hypothesis.errors.FailedHealthCheck: It looks like your strategy is filtering out a lot of data. Health check found 50 filtered examples but only 9 good ones. This will make your tests much slower, and also will probably distort the data generation quite a lot. You should adapt your strategy to filter less.
hypothesis.errors.FailedHealthCheck: Data generation is extremely slow However, if the complexity achieved is necessary for this purpose, we can suppress the control in question for those single tests that would risk random failures, by meddling with the settings decorator:
legibility make errors helpful and Hypothesis grokable. Comments. Copy link ... hypothesis.errors.FailedHealthCheck: Data generation is extremely slow: Only produced 9 valid examples in 1.13 seconds (0 invalid ones and 1 exceeded maximum size). Try decreasing size of the data you're generating (with e.g.average_size or max_leaves parameters ...
E hypothesis.errors.FailedHealthCheck: Data generation is extremely slow: Only produced 4 valid examples in 1.01 seconds (1 invalid ones and 0 exceeded maximum size). Try decreasing size of the data you're generating (with e.g.max_size or max_leaves parameters).
All groups and messages ... ...
hypothesis.errors.FailedHealthCheck: It looks like your strategy is filtering out a lot of data. Health check found 50 filtered examples but only 3 good ones. This will make your tests much slower, and also will probably distort the data generation quite a lot. You should adapt your strategy to filter less.
The healthcheck is not relevant here, because h2 clearly knows about it: # We need to refresh the encoder because hypothesis has a problem with # integrating with py.test, meaning that we use the same frame factory # for all tests.
Gentoo's Bugzilla - Bug 930416 dev-python/pyarrow-16.. fails tests: FAILED test_strategies.py::test_types - hypothesis.errors.FailedHealthCheck: Data generation is extremely slow: Onl.. Last modified: 2024-04-22 11:24:04 UTC node [vulture]
E hypothesis.errors.FailedHealthCheck: Data generation is extremely slow: Only produced 6 valid examples in 1.04 seconds (14 invalid ones and 2 exceeded maximum size). Try decreasing size of the data you're generating (with e.g. max_size...
If you want to disable just this health check, add HealthCheck.too_slow to the suppress_health_check settings for this test. Does anyone know how to work with hypothesis to fix this problem? -- Efraim Flashner <address@hidden> אפרים פלשנר GPG key = A28B F40C 3E55 1372 662D 14F7 41AA E7DC CA3D 8351 Confidentiality cannot be guaranteed ...
[Python] tests/test_feather.py::test_roundtrip: hypothesis.errors.FailedHealthCheck: Data generation is extremely ... to_pylist_roundtrip(arr): E hypothesis.errors.FailedHealthCheck: Data generation is extremely slow: Only produced 9 valid examples in 1.86 seconds (0 invalid ones and 7 exceeded maximum size). Try decreasing size of the data you ...
When generating eval input I consistently get the following error: hypothesis.errors.FailedHealthCheck: It looks like your strategy is filtering out a lot of data. Health check found 50 filtered examples but only 3 good ones. This will make your tests much slower, and also will probably distort the data generation quite a lot.
9. As the documentation and this article state, it should be possible to use hypothesis strategies and pytest fixtures in the same test. But executing this example code of the article: from hypothesis import given, strategies as st. from pytest import fixture. @fixture. def stuff(): return "kittens".
I've attempted running tests with hypothesis 4.44.2 and they frequently fail with the following error ...
Unreliable test timings! On an initial run, this test took 1624.68ms, which exceeded the deadline of 200.00ms, but on a subsequent run it took 0.15 ms, which did not. If you expect this sort of variability in your test timings, consider turning deadlines off for this test by setting deadline=None. I'm just creating an object and testing its ...