hypothesis | use library for property-based testing | Testing library

 by   HypothesisWorks Python Version: 6.103.2 License: Non-SPDX

kandi X-RAY | hypothesis Summary

kandi X-RAY | hypothesis Summary

hypothesis is a Python library typically used in Testing applications. hypothesis has no vulnerabilities and it has medium support. However hypothesis has 24 bugs, it build file is not available and it has a Non-SPDX License. You can install using 'pip install hypothesis' or download it from GitHub, PyPI.

Hypothesis is a powerful, flexible, and easy to use library for property-based testing.

            kandi-support Support

              hypothesis has a medium active ecosystem.
              It has 6750 star(s) with 566 fork(s). There are 71 watchers for this library.
              There were 10 major release(s) in the last 6 months.
              There are 45 open issues and 1360 have been closed. On average issues are closed in 54 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of hypothesis is 6.103.2

            kandi-Quality Quality

              hypothesis has 24 bugs (6 blocker, 0 critical, 17 major, 1 minor) and 494 code smells.

            kandi-Security Security

              hypothesis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              hypothesis code analysis shows 0 unresolved vulnerabilities.
              There are 42 security hotspots that need review.

            kandi-License License

              hypothesis has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              hypothesis releases are available to install and integrate.
              Deployable package is available in PyPI.
              hypothesis has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed hypothesis and discovered the below as its top functions. This is intended to give you an instant insight into hypothesis implemented functionality, and help decide if they suit your requirements.
            • Return a pandas DataFrame containing a subset of rows
            • Check if argument is a SearchStrategy
            • Return a sequence of elements and dtype
            • Warns a deprecation warning
            • Construct a search strategy
            • Return a floating point value
            • Run a function in a process
            • Identify that the FTZ module is imported
            • Create a search strategy for complex numbers
            • Specify a search strategy
            • Normalize a test function
            • Define function signature
            • Create a ndarray from a dtype
            • Generate a searchStrategy for a given shape
            • Define a function signature
            • Generates a test magic string
            • Runs all the examples in the pareto front
            • Updates the changelog and version information
            • Create a search strategy for integer indices
            • Return an example
            • Shrink the buffer
            • Construct a search strategy for a given shape
            • Reduce the lexical blocks
            • Execute only once
            • Creates a searchStrategy for mutually - broadcastable shapes
            • Draw a numpy array
            • Performs random selection
            • Convert an object into a list
            Get all kandi verified functions for this library.

            hypothesis Key Features

            No Key Features are available at this moment for hypothesis.

            hypothesis Examples and Code Snippets

            Pythondot img1Lines of Code : 167dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            def heapnew():
                return []
            def heapempty(heap):
                return not heap
            def heappush(heap, value):
                index = len(heap) - 1
                while index > 0:
                    parent = (index - 1) // 2
                    if heap[parent] > heap[index]:
            Pythondot img2Lines of Code : 93dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            class TestData:
                def draw_bytes(self, n):
            class SearchStrategy:
                def do_draw(self, data):
                    raise NotImplementedError()
            class Int64Strategy:
                def do_draw(self, data):
                    return int.from_bytes(data.draw_bytes(8), byte  
            Pythondot img3Lines of Code : 87dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            from hypothesis import given
            from hypothesis.strategies import floats
            @given(floats(), floats())
            def test_floats_are_commutative(x, y):
                assert x + y == y + x
            python -m pytest test_floats.py
                @given(floats(), floats())
                def test_floats_a  
            Calculate edit distance between hypothesis and truth matrix .
            pythondot img4Lines of Code : 101dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"):
              """Computes the Levenshtein distance between sequences.
              This operation takes variable-length sequences (`hypothesis` and `truth`),
              each provided as a `SparseTensor`, a  
            Test the hypothesis solution .
            javadot img5Lines of Code : 24dot img5License : Permissive (MIT License)
            copy iconCopy
            private static void test(final Integer[] preorder, final Integer[] inorder) {
                    System.out.println("Naive Solution...");
                    BinaryTree root = new BinaryT  
            Calculate the hypothesis value of a parameter .
            pythondot img6Lines of Code : 14dot img6License : Permissive (MIT License)
            copy iconCopy
            def _hypothesis_value(data_input_tuple):
                Calculates hypothesis function value for a given input
                :param data_input_tuple: Input tuple of a particular example
                :return: Value of hypothesis function at that point.
                Note that there   

            Community Discussions


            Recommended way of measuring execution time in Tensorflow Federated
            Asked 2021-Jun-15 at 13:49

            I would like to know whether there is a recommended way of measuring execution time in Tensorflow Federated. To be more specific, if one would like to extract the execution time for each client in a certain round, e.g., for each client involved in a FedAvg round, saving the time stamp before the local training starts and the time stamp just before sending back the updates, what is the best (or just correct) strategy to do this? Furthermore, since the clients' code run in parallel, are such a time stamps untruthful (especially considering the hypothesis that different clients may be using differently sized models for local training)?

            To be very practical, using tf.timestamp() at the beginning and at the end of @tf.function client_update(model, dataset, server_message, client_optimizer) -- this is probably a simplified signature -- and then subtracting such time stamps is appropriate?

            I have the feeling that this is not the right way to do this given that clients run in parallel on the same machine.

            Thanks to anyone can help me on that.



            Answered 2021-Jun-15 at 12:01

            There are multiple potential places to measure execution time, first might be defining very specifically what is the intended measurement.

            1. Measuring the training time of each client as proposed is a great way to get a sense of the variability among clients. This could help identify whether rounds frequently have stragglers. Using tf.timestamp() at the beginning and end of the client_update function seems reasonable. The question correctly notes that this happens in parallel, summing all of these times would be akin to CPU time.

            2. Measuring the time it takes to complete all client training in a round would generally be the maximum of the values above. This might not be true when simulating FL in TFF, as TFF maybe decided to run some number of clients sequentially due to system resources constraints. In practice all of these clients would run in parallel.

            3. Measuring the time it takes to complete a full round (the maximum time it takes to run a client, plus the time it takes for the server to update) could be done by moving the tf.timestamp calls to the outer training loop. This would be wrapping the call to trainer.next() in the snippet on https://www.tensorflow.org/federated. This would be most similar to elapsed real time (wall clock time).

            Source https://stackoverflow.com/questions/67982276


            Extract p-value from an Object QuadTypeIndependenceTest and ScalarIndependenceTest from Coin Packages
            Asked 2021-Jun-13 at 04:26

            Using Aids2 dataset from package MASS, I am applying Ansari-Bradley Non-Parametric Test to test Group Independency by this snippets



            Answered 2021-Jun-13 at 04:26

            Since object like "QuadTypeIndependenceTest" and "ScalarIndependenceTest" are created from the results of coin packages, there is specific function to extract the pvalue, using coin::pvalue(obj), Special thanks for pointing @AntoniosK

            Source https://stackoverflow.com/questions/67835281


            Strange behaviour of mat-form-field when pressing Return key
            Asked 2021-Jun-11 at 17:23

            I have a form with multiple form fields organized in rows and columns and a delete row button at the end of each row. When the user enter a value in on the form fields if the press the Enter/Return key, instead of "entering" the value, it delete the row, as if the focus of the user was on the delete button and he'd pressed enter.

            Here's a screen shot of the form:

            Here is the html code for this part of the application:



            Answered 2021-Jun-11 at 17:23

            Buttons inside forms are by default submit in Angular unless you specify otherwise. Pressing enter in a field fires a submit event. So your removeHypothesis is considered a submit function and thus it is fired on enter press.

            Make the button type="button" and it will no longer be considered as a submit button:

            Source https://stackoverflow.com/questions/67941052


            Produce a function in Coq which outputs every witness to an existence-uniqueness axiom
            Asked 2021-Jun-11 at 09:25

            So, I'm pretty sure this should be possible without choice. Maybe I am wrong.

            Here is a minimal reproducible example of what I'm trying to do:



            Answered 2021-Jun-11 at 09:25

            In the two links you mention, the problem is the segregation enforced by Coq between propositions (those types of type Prop) and other types (those with type Set or Type), with the idea being that proofs should not be needed for programs to run. However, in your case both set M and subset M are propositions, so this separation is not a problem: as you saw when defining fn0, Coq is perfectly happy to use the first component of your existential type to build the function you are looking for. This is a good point of constructive mathematics: modulo the separation between Prop and Type, choice is simply true!

            Rather, the problem comes from the second part of the proof, i.e. the proof of equality of functions. It is a subtle issue of Coq that equality of functions is not extensional, that is the following axiom cannot, in general, be proven

            Source https://stackoverflow.com/questions/67905273


            Kafka consumer/producer with Python in WSL2 Ubuntu
            Asked 2021-Jun-09 at 22:12

            This is a follow-up question from this thread.

            As per advised from the thread, the possible cause for why my Python code is not working is because I was to connect to a remote server in WSL2. And there could be unknown issues with WSL2 Ubuntu.

            So I am testing that hypothesis with the following two approaches of communicating within WLS2 Ubuntu locally (i.e. via localhost:9092):

            Note that, for both approaches below, I already have zookeeper running in one terminal (T1) with:



            Answered 2021-Jun-09 at 22:12

            produce the message through a command ... I surprisingly receive it in the consumer terminal T7

            No surprise here since you've not called producer.flush() or producer.close() in your Python producer app after starting the consumer loop.

            The console producer blocks on every record by calling get() on the future - source, effectively flushing its buffer

            Alternatively, you are missing the matching option for --from-beginning in the Python consumer if you wanted to see the previously sent records

            Ultimately, testing a local client/server within the same network adapter/subnet isn't going to help resolve an external network connection

            Source https://stackoverflow.com/questions/67911494


            Why did Hypothesis give a falsifying example, when manually reproducing with numpy arrays does not fail?
            Asked 2021-Jun-04 at 17:57

            I was trying to write my first ultra-simple numpy testcase, but the first thing I thought of seems to hit a roadblock.

            So I did this:



            Answered 2021-May-25 at 13:18

            Hypothesis is showing you that Numpy datatypes have distinct byte orders. Expanding your test,

            Source https://stackoverflow.com/questions/67634660


            What makes the different performances between numpy.sum and numpy.cumsum?
            Asked 2021-Jun-02 at 23:44

            If my understanding is correct, both np.sum and np.cumsum will take O(n) time. However, when I do both operations to the same matrix sequentially (on different axis), it seems the sequence of np.sum and np.cumsum makes the overall performance quite different, though the result is the same as expected.

            If I do the np.cumsum along column direction (axis=1) for each row first, and then do np.sum for all the rows (axis=0), it will take longer time.

            If I do the np.sum along rows (axis=0) first, then do np.cumsum for the one dimension array, it will take shorter time.

            My hypothesis is that the np.cumsum will take more time on the data allocation/manipulation since it will yield more data than np.sum, so it will take longer time if there are more operations of np.cumsum.

            Here is my testing code and result



            Answered 2021-Jun-02 at 21:55

            If my understanding is correct, both np.sum and np.cumsum will take O(n) time.

            Correct (assuming the input is the same)!

            it seems the sequence of np.sum and np.cumsum makes the overall performance quite different

            np.cumsum is more expensive than np.sum on the same input since np.cumsum needs to allocate and write an output array (which can be pretty big). Moreover, assuming floating-point operations are associative, the implementation of np.sum can be easily optimized while it is quite hard to optimize np.cumsum.

            My hypothesis is that the np.cumsum will take more time on the data allocation/manipulation since it will yield more data than np.sum, so it will take longer time if there are more operations of np.cumsum.

            The thing is that the first implementation have much more work to do than the second. This is mainly why it is slower, especially because of the memory reads/writes. Indeed, np.cumsum produces a big 2D array in the first implementation that must be computed by np.sum. Writing and reading this temporary array is expensive. In the example, the first implementation require to move 14.9 GiB from/to the memory hierarchy (likely in RAM) just for the temporary array. Note that building a temporary array also make the computation less cache-friendly as it requires more space and thus b and c may not both fit in the cache any more (b and c takes 8 MiB each on my machine and my CPU have a L3 cache of 9 MiB which mean that not both can fit in the cache) as opposed to the second implementation. The throughput of the RAM is often much smaller than the one of the CPU caches.

            Note that the second implementation also produces temporary arrays. This version should allocate as much temporary arrays as the first one. However, the second implementation produces temporary arrays that are 1000 time smaller! Thus, the call to np.cumsum is much faster in this version. On my machine, b is mostly stored in the fast L3 cache and temporary arrays in the very fast L1 cache.

            Source https://stackoverflow.com/questions/67812429


            How to reorder an output in t.test in R?
            Asked 2021-Jun-01 at 02:19

            This is my first time using R, and I have a question regarding the output of t.test in R.

            I am running the t.test function and getting an output like this:



            Answered 2021-Jun-01 at 02:19

            The t.test() function creates a list, which can be accessed like other lists:

            Source https://stackoverflow.com/questions/67781754


            Manipulating C function
            Asked 2021-May-29 at 03:13

            Is there exist a way to manipulate C function some how
            for eg - we know C printf() function return Number of character printed to the console. So is there any way that i can get number of character but not letting printf() function print to console. using same printf() from stdio.h

            I know return is the last statement to get executed in a function hence what i am asking may be impossible but i do want to hear from the community i.e is my hypothesis i.e manipulating c function is possible or not?



            Answered 2021-May-29 at 03:13

            If you have access to the source code and you're able to recompile it with your changes, then sure, you can do it. Consider this:

            Source https://stackoverflow.com/questions/67747248


            How do Functions work in a Mongoose Schema?
            Asked 2021-May-28 at 04:06

            I have a schema where I'm trying to generate a nanoid to show on the front end. (The default MongoDB ObjectID is too long to display to users.)

            What I've done is to insert it into my schema as a default value that generates a new string each time an instance of the model is created.

            These are the instructions provided by the nanoid docs for dealing with Mongoose



            Answered 2021-May-28 at 04:06

            problem here was there was no logic to save when duplicate skip was false.

            Source https://stackoverflow.com/questions/67531647

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install hypothesis

            You can install using 'pip install hypothesis' or download it from GitHub, PyPI.
            You can use hypothesis like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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