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Testing

Testing your code is essential before deployment. It helps you catch errors and faulty conclusions before they make any major impact.

Today, employers are looking for data scientists with the skills to properly prepare their code for an industry setting, which includes testing their code.

Difficult errors:

Difficult errors because these errors are difficult to find because depends on quality and accuracy of analysis

  • Bad encoding
  • Inappropriate features
  • Unexpected features
  • etc...

Testing and Data Science

  • Problems that could occur in data science aren’t always easily detectable; you might have values being encoded incorrectly, features being used inappropriately, unexpected data breaking assumptions
  • To catch these errors, you have to check for the quality and accuracy of your analysis in addition to the quality of your code. Proper testing is necessary to avoid unexpected surprises and have confidence in your results.
  • TEST DRIVEN DEVELOPMENT: a development process where you write tests for tasks before you even write the code to implement those tasks.
  • UNIT TEST: a type of test that covers a “unit” of code, usually a single function, independently from the rest of the program.

Resources:

Unit Tests

  • We want to test our functions in a way that is repeatable and automated. Ideally, we'd run a test program that runs all our unit tests and cleanly lets us know which ones failed and which ones succeeded. Fortunately, there are great tools available in Python that we can use to create effective unit tests!

Unit Test Advantages and Disadvantages

  • The advantage of unit tests is that they are isolated from the rest of your program, and thus, no dependencies are involved. They don't require access to databases, APIs, or other external sources of information.

  • However, passing unit tests isn’t always enough to prove that our program is working successfully. To show that all the parts of our program work with each other properly, communicating and transferring data between them correctly, we use integration tests.

  • In this lesson, we'll focus on unit tests; however, when you start building larger programs, you will want to use integration tests as well.

    You can read about integration testing and how integration tests relate to unit tests here. That article contains other very useful links as well.

Unit Testing Tools

To install pytest, run

pip install -U pytest

in your terminal. You can see more information on getting started here.

  • Create a test file starting with test_
  • Define unit test functions that start with test_ inside the test file
  • Enter pytest into your terminal in the directory of your test file and it will detect these tests for you!

test_ is the default - if you wish to change this, you can learn how to in this pytest configuration

In the test output, periods represent successful unit tests and F's represent failed unit tests. Since all you see is what test functions failed, it's wise to have only one assert statement per test. Otherwise, you wouldn't know exactly how many tests failed, and which tests failed.

Your tests won't be stopped by failed assert statements, but it will stop if you have syntax errors.

Test Driven Development and Data Science

  • TEST DRIVEN DEVELOPMENT: writing tests before you write the code that’s being tested. Your test would fail at first, and you’ll know you’ve finished implementing a task when this test passes.
  • Tests can check for all the different scenarios and edge cases you can think of, before even starting to write your function. This way, when you do start implementing your function, you can run this test to get immediate feedback on whether it works or not in all the ways you can think of, as you tweak your function.
  • When refactoring or adding to your code, tests help you rest assured that the rest of your code didn't break while you were making those changes. Tests also helps ensure that your function behavior is repeatable, regardless of external parameters, such as hardware and time.

Test driven development for data science is relatively new and has a lot of experimentation and breakthroughs appearing, which you can learn more about in the resources below.