Testing

AB testing is crucial for assessing the effectiveness of changes in a controlled environment. With the leAB library, you can determine the optimal sample size prior to the test launch and subsequently compare the successes of Group A and Group B.

To ensure that your application logs an error under a specific condition, use the built-in fixture called caplog in pytest. 

This fixture allows you to capture log messages generated during the execution of your test.

When testing a Python class, it can be repetitive and time-consuming to create multiple instances to test a large number of instance methods. 

A better approach is to use the setUp method to instantiate a class object before running each test.

Do you forget what feature to implement when taking a break from work?

To keep your train of thought, write a unit test that describes the desired behavior of the feature and makes it fail intentionally.

This will give you a clear idea of what to work on when returning to the project, allowing you to get back on track faster.

My previous tips on testing in Python.

An accurate machine learning model can still cause problems if it exhibits unexpected behaviors not identified during testing.

For example, a sentiment analysis model may perform well during training, but upon deploying it, the model incorrectly labels certain positive reviews as negative when rephrasing using negative words. 

Testing the behaviors of an ML model can help us identify areas for improvement. This article and video will show you how to test the behaviors of your ML model with behave.

Article.

Video.

If you want to test your function with different examples, use the pytest.mark.parametrize decorator.

My previous tips on testing.

If you want to write declarative assertions and avoid boilerplate code in your unit tests, try dirty_equals.

Link to dirty-equals.

My previous tips on testing.

pytest parametrize allows you to test your function with different examples. When using pytest parametrize, it can be difficult to understand the role of each test case.

You can add id to each test case using pytest.param for clearer outputs.

My previous tips on testing.

Sometimes you might want to test if the function that interacts with files is working properly but don’t want the tests to touch the real disk.

pyfakefs allows your tests to operate on a file system in memory without touching the real disk.

In the code above, I created a fake directory and tested if create_file is creating a new file in the fake directory.