A workshop to demonstrate how we can write unit tests in python, and how we can refactor jupyter notebooks to be modular and tested. Code: https://github.com/davified/unit-testing-workshop

1. Unit Testing Workshop
It’s not that scary

2. 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)

3. 3
WHY TEST?
Complexity
+
Change

4. ©ThoughtWorks 2019 Commercial in Confidence
4
Feedback
Time
Unit
tests
Jupyter
notebooks
IT’S ALL ABOUT FEEDBACK

5. 5
TEST PYRAMID
Unit tests
ML metrics tests
Any other
automated tests
Manual tests
Automate
d
Time
required

6. How do we unit test?

7. 7
ANATOMY OF A UNIT TEST
assert_that(do_something(input) => expected_output)

8. 8
ANATOMY OF A UNIT 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

9. 9
ANATOMY OF A UNIT TEST
def add(n1, n2):
return n1 + n2
self.assertEqual(2, add(1,1))

10. 10
EXERCISE 1: UNIT TESTING A CALCULATOR
1. Code walkthrough: src/test_calculator.py and src/calculator.py

11. 11
EXERCISE 1: UNIT TESTING 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

12. 12
EXERCISE 1: UNIT TESTING A CALCULATOR - YOUR TURN!
● Implement
○ subtract(a, b)
○ multiply(a, b)
○ divide(a, b)
● Challenge: write the tests first!

13. 13
GOOD TESTING PRACTICES
● Do the simplest thing possible
● Red green refactor
● Descriptive test names
● Pair program (+ ping pong)

14. 14
ANTI-PATTERNS / BAD TESTING 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)

15. 15
EXERCISE 2: UNIT TESTING + REFACTORING A JUPYTER NOTEBOOK
notebooks/sklearn-nlp-pipeline-before-refactoring.ipynb

16. 16
EXERCISE 2: UNIT TESTING + 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

17. 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

18. 18
NEXT STEPS
● Learn by 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