suppress health_check hypothesis

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Suppressing HealthCheck.too_slow for a composite hypothesis strategy

I have a number of unit tests that leverage a @composite strategy that I wrote. The strategy is quite slow (it generates complex objects) and from time to time one of the tests fails the too_slow health check. When that happens, I take a deep sigh and I add

to the test.

Is there a way to suppress HealthCheck.too_slow once and for all, for all the tests that use the slow strategy?

• python-hypothesis

• 1 this looks like XY-problem: can you describe one of your composite strategies so we can find a way to rewrite it and make faster? –  Azat Ibrakov Commented Oct 3, 2018 at 4:25

You can change the global default settings using settings profiles , but there is no way to apply certain settings to a subset of your tests built-in to Hypothesis.

If you know you have a slow strategy I'd try to make that strategy faster - usually there are some serious optimisations available - and if I couldn't I'd just disable the particular health check. Just re-enable it occasionally to check for unexpectedly slow tests!

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Python hypothesis.HealthCheck.too_slow() Examples

Immutable Python Dictionary

Pytest fixture scope can cause test config side effects

When running unit tests in Python you might have a lot of config and environment variables in a shared fixture that is loaded in for every test. Session scoped fixtures are nice for loading something once and keeping it around for all tests, but that can be a problem if one of your tests decides to change something in the config.

Running this test by itself might succeed, but then it might fail when your entire test suite runs if other tests depend on that setting being a certain way.

The simple fix is to make the config() fixture here not session scoped. Thus every test will get a fresh copy of the config.

But what if that isn’t an option?

I work a lot with PySpark and have a lot of unit tests that test dataframes. To help with this, I use a tool called Hypothesis which, among other things, can intelligently generate sample dataframes to help you find edge cases in your logic.

So I have tests that can look like this:

This test has two separate things going on at the high level:

pytest.mark.parameterize() will call the test 4 times with the parameters defined. It will reload any fixtures that aren’t session scoped for each of those calls. It’s like having 4 separate tests, but since the test code is identical you can refactor them into a single parameterized test like this one.

given(data_frames([])) is a Hypothesis decorator that generates a Pandas dataframe ( pdf ) for the test. Not only that, but it’ll generate MANY dataframes, using the rules defined (min/max, sample from, etc.) to test the boundaries of the different datatypes. Here I’ve set max_examples to 5 so it doesn’t do this too many times.

However, running the test generates the following warning:

So unlike parameterize() , Hypothesis will NOT reload fixtures between function calls. In my case this wasn’t a real concern since none of these tests were making changes to the config fixture. But what if another developer makes a change? What if later I need to? I had in the past intentionally not made config() session scoped to avoid weird side effects, but this new warning prompted me to revisit the question:

Can you make a Python Dictionary Immutable?

The answer is “Yes!”

Now if a certain test needs to make a change in the config fixture, the developer will have to make a copy that is scoped to just that test. There are a couple of ways to do this, I’ve put two of them above (the second one I like a lot).

Many will tell you that you should never need an immutable dictionary and for the most part I agree. Chances are if you need this capability you can get a better result by doing a smart refactor.

I briefly thought about where this same change might benefit my main code, but have thus far not done so as the application doesn’t have this problem and using this feature would add complexity with no real benefit.

However I am happy to have this new tool in my back pocket.

• https://realpython.com/python-data-structures/#typesmappingproxytype-a-wrapper-for-making-read-only-dictionaries
• https://stackoverflow.com/questions/9997176/immutable-dictionary-only-use-as-a-key-for-another-dictionary

Tags spark python hypothesis pytest immutable

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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.

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Cannot assign hypothesis.settings.suppress_health_check #789

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What to Know About Ukraine’s Cross-Border Assault Into Russia

The incursion caught Russia by surprise and signified a shift in tactics for Kyiv after more than two years of war with Russia.

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By Andrew E. Kramer Constant Méheut Kim Barker Anton Troianovski and Cassandra Vinograd

Ukraine pressed ahead with its offensive inside Russian territory on Sunday , pushing toward more villages and towns nearly two weeks into the first significant foreign incursion in Russia since World War II.

But even as the Ukrainian army was advancing in Russia’s western Kursk region, its troops were steadily losing ground on their own territory. The Russian military is now about eight miles from the town of Pokrovsk in eastern Ukraine, according to open-source battlefield maps . The capture of Pokrovsk, a Ukrainian stronghold, would bring Moscow one step closer to its long-held goal of capturing the entire Donetsk region.

That underscored the gamble Ukraine’s army took when it crossed into Russia: throwing its forces into a daring offensive that risked weakening its own positions on the eastern front.

Whether that strategy will prove advantageous remains to be seen, analysts say.

On the political front, the offensive has already had some success: Ukraine’s rapid advance has embarrassed the Kremlin and has altered the narrative of a war in which Kyiv’s forces had been on the back foot for months.

Here’s what to know about Ukraine’s cross-border operation, which President Biden said last week was creating a “real dilemma” for the Russian government.

What happened?

Ukrainian troops and armored vehicles stormed into the Kursk region of western Russia on Aug. 6 , swiftly pushing through Russian defenses and capturing several villages.

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Source: Institute for the Study of War with American Enterprise Institute’s Critical Threats Project

By Veronica Penney

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• Quick start guide
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Quick start guide ¶

This document should talk you through everything you need to get started with Hypothesis.

An example ¶

Suppose we’ve written a run length encoding system and we want to test it out.

We have the following code which I took straight from the Rosetta Code wiki (OK, I removed some commented out code and fixed the formatting, but there are no functional modifications):

We want to write a test for this that will check some invariant of these functions.

The invariant one tends to try when you’ve got this sort of encoding / decoding is that if you encode something and then decode it then you get the same value back.

Let’s see how you’d do that with Hypothesis:

(For this example we’ll just let pytest discover and run the test. We’ll cover other ways you could have run it later).

The text function returns what Hypothesis calls a search strategy. An object with methods that describe how to generate and simplify certain kinds of values. The @given decorator then takes our test function and turns it into a parametrized one which, when called, will run the test function over a wide range of matching data from that strategy.

Anyway, this test immediately finds a bug in the code:

Hypothesis correctly points out that this code is simply wrong if called on an empty string.

If we fix that by just adding the following code to the beginning of our encode function then Hypothesis tells us the code is correct (by doing nothing as you’d expect a passing test to).

If we wanted to make sure this example was always checked we could add it in explicitly by using the @example decorator:

This can be useful to show other developers (or your future self) what kinds of data are valid inputs, or to ensure that particular edge cases such as "" are tested every time. It’s also great for regression tests because although Hypothesis will remember failing examples , we don’t recommend distributing that database.

It’s also worth noting that both @example and @given support keyword arguments as well as positional. The following would have worked just as well:

Suppose we had a more interesting bug and forgot to reset the count each time. Say we missed a line in our encode method:

Hypothesis quickly informs us of the following example:

Note that the example provided is really quite simple. Hypothesis doesn’t just find any counter-example to your tests, it knows how to simplify the examples it finds to produce small easy to understand ones. In this case, two identical values are enough to set the count to a number different from one, followed by another distinct value which should have reset the count but in this case didn’t.

Installing ¶

Hypothesis is available on PyPI as “hypothesis” . You can install it with:

You can install the dependencies for optional extensions with e.g. pip install hypothesis[pandas,django] .

If you want to install directly from the source code (e.g. because you want to make changes and install the changed version), check out the instructions in CONTRIBUTING.rst .

Running tests ¶

In our example above we just let pytest discover and run our tests, but we could also have run it explicitly ourselves:

We could also have done this as a unittest.TestCase :

A detail: This works because Hypothesis ignores any arguments it hasn’t been told to provide (positional arguments start from the right), so the self argument to the test is simply ignored and works as normal. This also means that Hypothesis will play nicely with other ways of parameterizing tests. e.g it works fine if you use pytest fixtures for some arguments and Hypothesis for others.

Writing tests ¶

A test in Hypothesis consists of two parts: A function that looks like a normal test in your test framework of choice but with some additional arguments, and a @given decorator that specifies how to provide those arguments.

Here are some other examples of how you could use that:

Note that as we saw in the above example you can pass arguments to @given either as positional or as keywords.

Where to start ¶

You should now know enough of the basics to write some tests for your code using Hypothesis. The best way to learn is by doing, so go have a try.

If you’re stuck for ideas for how to use this sort of test for your code, here are some good starting points:

Try just calling functions with appropriate arbitrary data and see if they crash. You may be surprised how often this works. e.g. note that the first bug we found in the encoding example didn’t even get as far as our assertion: It crashed because it couldn’t handle the data we gave it, not because it did the wrong thing.

Look for duplication in your tests. Are there any cases where you’re testing the same thing with multiple different examples? Can you generalise that to a single test using Hypothesis?

This piece is designed for an F# implementation , but is still very good advice which you may find helps give you good ideas for using Hypothesis.

If you have any trouble getting started, don’t feel shy about asking for help .

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Ukrainian president says the push into Russia’s Kursk region is to create a buffer zone there

Ukrainian President Volodymyr Zelenskyy on Sunday announced plans to create a “buffer zone” in Russia’s Kursk region. “Our primary task in defensive operations overall - to destroy as much Russian war potential as possible and conduct maximum counteroffensive actions,” he said in a nightly address.

The Ukrainian armed forces released footage on Sunday of what is said to show the destruction of a key bridge in Russia’s Kursk region. Ukraine also struck a second bridge nearby, less than two weeks into its stunning cross-border incursion.

In this footage released by the Ukrainian armed forces on Sunday, Aug. 18, 2024, smoke billows in . what is said to show the destruction of a key bridge in Russia’s Kursk region. Ukraine has destroyed a key bridge in Russia’s Kursk region and struck a second one nearby, less than two weeks into its stunning cross-border incursion, disrupting Russian supply routes and possibly signaling that its troops are planning to dig in. (Ukrainian Armed Force via AP)

In this photo provided by the Ukrainian Defence Ministry Press Office, a strategically important bridge over the river Seym is destroyed by Ukrainian troops as they continue their incursion into the Kursk region, Russia, Friday, Aug. 16, 2024. The bridge was used by the Kremlin to supply its troops and its destruction could hamper their efforts. (Ukrainian Defence Ministry Press Office via AP)

In this photo taken from video released by the Russian Defense Ministry on Sunday, Aug. 18, 2024, Russian soldiers fire Giatsint-S self-propelled gun towards Ukrainian positions at an undisclosed location in the Russian - Ukrainian border area in the Kursk region, Russia. (Russian Defense Ministry Press Service photo via AP)

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KYIV, Ukraine (AP) — Ukrainian President Volodymyr Zelenskyy said Sunday the daring military incursion into Russia’s Kursk region aims to create a buffer zone to prevent further attacks by Moscow across the border.

It was the first time Zelenskyy clearly stated the aim of the operation that began Aug. 6. Previously, he had said the operation aimed to protect communities in the bordering Sumy region from constant shelling.

Zelenskyy said “it is now our primary task in defensive operations overall: to destroy as much Russian war potential as possible and conduct maximum counteroffensive actions. This includes creating a buffer zone on the aggressor’s territory -– our operation in the Kursk region,” he said in his nightly address.

This weekend, Ukraine destroyed a key bridge in the region and struck a second one nearby, disrupting supply lines as it pressed the incursion, officials said.

Pro-Kremlin military bloggers acknowledged the destruction of the first bridge on the Seim River near the town of Glushkovo will impede deliveries of supplies to Russian forces repelling Ukraine’s incursion, although Moscow could still use pontoons and smaller bridges. Ukraine’s air force chief, Lt. Gen. Mykola Oleshchuk, on Friday released a video of an airstrike that cut the bridge in two.

Less than two days later, Ukrainian troops hit a second bridge in Russia, according to Oleshchuk and Russian regional Gov. Alexei Smirnov.

As of Sunday morning, there were no officials giving the exact location of the second bridge attack. But Russian Telegram channels claimed that a second bridge over the Seim, in the village of Zvannoe, had been struck.

According to Russia’s Mash news site, the attacks left only one intact bridge in the area. The Associated Press could not immediately verify these claims. If confirmed, the Ukrainian strikes would further complicate Moscow’s attempts to replenish its forces and evacuate civilians.

Glushkovo is about 12 kilometers (7.5 miles) north of the Ukrainian border, and approximately 16 kilometers (10 miles) northwest of the main battle zone in Kursk. Zvannoe is located another 8 kilometers (5 miles) to the northwest.

Kyiv previously has said little about the goals of its push into Russia with tanks and other armored vehicles, the largest attack on the country since World War II, which took the Kremlin by surprise and saw scores of villages and hundreds of prisoners fall into Ukrainian hands.

The Ukrainians drove deep into the region in several directions, facing little resistance and sowing chaos and panic as tens of thousands of civilians fled. Ukraine’s Commander in Chief, Gen. Oleksandr Syrskyi, claimed last week that his forces had advanced across 1,000 square kilometers (390 square miles) of the region, although it was not possible to independently verify what Ukrainian forces effectively control.

Buffer zones sought by both sides

In his remarks on creating a buffer zone, Zelenskyy said Ukrainian forces “achieved good and much-needed results.”

Analysts say that although Ukraine could try to consolidate its gains inside Russia, it would be risky, given Kyiv’s limited resources, because its own supply lines extending deep into Kursk would be vulnerable.

The incursion has proven Ukraine’s ability to seize the initiative and has boosted its morale, which was sapped by a failed counteroffensive last summer and months of grinding Russian gains in the eastern Donbas region.

For his part, Russian President Vladimir Putin said while visiting China in May that Moscow’s offensive that month in Ukraine’s northeastern Kharkiv region was aimed at creating a buffer zone there.

That offensive opened a new front and displaced thousands of Ukrainians. The attacks were a response to Ukrainian shelling of Russia’s Belgorod region , Putin said.

“I have said publicly that if it continues, we will be forced to create a security zone, a sanitary zone,” he said. “That’s what we are doing.”

Ukraine’s move into Kursk resembled its lightning operation from September 2022, led by Syrskyi, in which its forces reclaimed control of the northeastern Kharkiv region after taking advantage of Russian manpower shortages and a lack of field fortifications.

Zelenskyy seeks permission to strike deeper into Russia

On Saturday, Zelenskyy urged Kyiv’s allies to lift remaining restrictions on using Western weapons to attack targets deeper in Russia, including in Kursk, saying his troops could deprive Moscow “of any ability to advance and cause destruction” if granted sufficient long-range capabilities.

“It is crucial that our partners remove barriers that hinder us from weakening Russian positions in the way this war demands. … The bravery of our soldiers and the resilience of our combat brigades compensate for the lack of essential decisions from our partners,” Zelenskyy said on the social platform X.

Russia’s Foreign Ministry and pro-Kremlin bloggers alleged U.S.-made HIMARS launchers have been used to destroy bridges on the Seim. These claims could not be independently verified.

Ukraine’s leaders have repeatedly sought authorization for long-range strikes on Russian air bases and other infrastructure used to pummel Ukraine’s energy facilities and other civilian targets, including with retrofitted Soviet-era “glide bombs” attacking Ukraine’s industrial east in recent months.

Moscow also appears to have increased attacks on Kyiv, targeting it Sunday with ballistic missiles for a third time this month, according to the head of the municipal military administration. Serhii Popko said in a Telegram post the “almost identical” August strikes on the capital “most likely used” North Korean-supplied KN-23 missiles.

Another attempt to target Kyiv followed at about 7 a.m. Popko said, this time with Iskander cruise missiles. Ukrainian air defenses struck down all the missiles fired in both attacks on the city, he said.

Fears mount for Zaporizhzhia nuclear plant

Elsewhere, the head of the U.N. nuclear watchdog agency said Saturday the safety situation at the Russian-occupied Zaporizhzhia Nuclear Power Plant is deteriorating.

International Atomic Energy Agency head Rafael Grossi urged “maximum restraint from all sides” after an IAEA team at the plant reported an explosive carried by a drone detonated just outside its protected area.

According to Grossi, the impact was “close to the essential water sprinkle ponds” and about 100 meters (100 yards) from the only power line supplying the plant. The IAEA team at the plant has reported intense military activity in the surrounding area in the past week, it said.

Kyiv and Moscow have traded blame for attacks near the power plant since it was captured by Russian forces early in the 2022 invasion, including a fire at the facility last weekend. Grossi said the blaze had caused “considerable damage,” but posed no immediate danger to nuclear safety.

Belarus says it’s deploying more troops on Ukraine border

Russian ally Belarus has massed “nearly a third” of its army along its border with Ukraine, according to authoritarian President Alexander Lukashenko.

Lukashenko told Russian state TV that Minsk was responding to the deployment of more than 120,000 Ukrainian troops to the 1,084-kilometer (674 mile) frontier. Belarus’ professional army numbers upward of 60,000.

Ukrainian border force spokesman Andrii Demchenko said Sunday it had not observed any sign of a Belarusian buildup.

Lukashenko, in power for three decades, has relied on Russian support to suppress the biggest protests in Belarus’ post-Soviet history after his 2020 reelection, widely seen as a sham both at home and abroad. He allowed Russian troops to use Belarus’ territory to invade Ukraine and let Moscow deploy some tactical nuclear weapons on its soil.

Follow developments in the war at https://apnews.com/hub/russia-ukraine