Workshop: Unit Testing in Python

Workshop: Unit Testing in Python

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  Unit Testing Workshop It’snot that scary
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  2 BEFORE THE WORKSHOP ●Download a code editor if you don’t have one ○ https://code.visualstudio.com/, or ○ https://www.jetbrains.com/pycharm/download/ ● git clone https://github.com/davified/unit-testing-workshop Windows users: ● docker run -it -v C:pathtounit-testing-workshop:/code -p 8888:8888 unit-testing-workshop ● Run all commands in the README up until: ○ nosetests --with-watch ● Configure your IDE (we’ll do this together)
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  3 WHY TEST? Complexity + Change
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  ©ThoughtWorks 2019 Commercialin Confidence 4 Feedback Time Unit tests Jupyter notebooks IT’S ALL ABOUT FEEDBACK
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  5 TEST PYRAMID Unit tests MLmetrics tests Any other automated tests Manual tests Automate d Time required
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  How do weunit test?
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  7 ANATOMY OF AUNIT TEST assert_that(do_something(input) => expected_output)
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  8 ANATOMY OF AUNIT TEST assert_that(do_something(input) => expected_output) 1. Think about what your function is doing 2. Write the simplest possible test case to assert that
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  9 ANATOMY OF AUNIT TEST def add(n1, n2): return n1 + n2 self.assertEqual(2, add(1,1))
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  10 EXERCISE 1: UNITTESTING A CALCULATOR 1. Code walkthrough: src/test_calculator.py and src/calculator.py
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  11 EXERCISE 1: UNITTESTING A CALCULATOR ● unittest module (built-in python module) ● How to write a unit test ○ File name must start with Test____ ○ Define a class that extends unittest.TestCase ○ Method name must start with test____(self) ● Assertions ○ self.assertEqual(expected, actual()) ● How to run tests ○ nosetests --with-watch
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  12 EXERCISE 1: UNITTESTING A CALCULATOR - YOUR TURN! ● Implement ○ subtract(a, b) ○ multiply(a, b) ○ divide(a, b) ● Challenge: write the tests first!
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  13 GOOD TESTING PRACTICES ●Do the simplest thing possible ● Red green refactor ● Descriptive test names ● Pair program (+ ping pong)
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  14 ANTI-PATTERNS / BADTESTING HABITS ● Redundant tests ● Bad test names ● Empty tests (no assertions) ● Unspecific tests (e.g. Asserting that add(2, 3) isNotNone) ● Non-deterministic tests (causes flaky tests) ● Tautological tests (i.e. repeating implementation details in test cases)
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  15 EXERCISE 2: UNITTESTING + REFACTORING A JUPYTER NOTEBOOK notebooks/sklearn-nlp-pipeline-before-refactoring.ipynb
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  16 EXERCISE 2: UNITTESTING + REFACTORING A JUPYTER NOTEBOOK Instructions ● Read the notebook to get a sense of what it’s doing (it’s training a simple NLP model for sentiment classification) ● Sit back and relax
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  17 ● Readable notebooks ●Automated tests -- no more visual eyeballing of long notebooks ● Smaller units → Separation of concerns ● Unit tests == living documentation ● Unit tests == safety harness ● Partition complexity (esp. if you TDD) ● Lesser bugs in production ● Possibility of refactoring → Ability to evolve to adapt to changing needs BENEFITS
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  18 NEXT STEPS ● Learnby doing ○ http://codewars.com ○ Start with 1 unit test in your project codebase ○ Explore unittest docs: https://docs.python.org/3/library/unittest.html (they’re good) ● Resources ○ Unit Testing for Data Scientists - Hanna Torrence ○ Lots of tutorials on the internet