TESTING

• start right away with the maxima.py function and the corresponding maxima_exercise.md
• how do you keep track of the manual testing?

general statements about testing

• Confidence: Write the code once and use it confidently everywhere else
• Confidence: Correctness is main requirement for scientific code

the agile development workflow

• add a test for the new functionality
• implement the new functionality (!) ⟶ yes, after implementing the test ;)
• run test suite ⟶ debug until all tests are green again
• optimize and/or refactor

test automation

• pytest
• side effect: trust
• side effect: faster development cycles
• side effect: better code

Continuous integration

We will use Travis CI (but there are many others). Note that it's for free only for public repositories:

• login to https://travis-ci.com/ with your GitHub account
• authorize Travis CI to have access to the repos
• put a .travis.yml file in the repo
• now your PR will automatically trigger a travis CI build
• you can manually trigger a build if you go to the travis repo page: https://travis-ci.com/USERNAME/REPONAME/ under More options...

testing scientific code

• floating point equality (1.1+2.2 != 3.3)): np.isclose, np.allclose

• issues with numpy arrays:

       >>> x = np.array([1, 1])
       >>> y = np.array([2, 2])
       >>> z = np.array([3, 3])
       >>> x+y==z
       array([True, True])
       >>> assert x+y == z
       ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
       >>> assert np.all(x+y==z)
       >>> np.allclose(x+y, z)
       True

• classical reference: What Every Computer Scientist Should Know About Floating-Point Arithmetic ⟶ rewrite for humans: Why don’t my numbers add up?

random bits

• tests should be short and quick to execute
• tests should be easy to read
• tests should exercise one thing
• test simple but general cases
• test corner cases and boundary conditions
• numerical fuzzing and the importance of the random seed (np.random.RandomState and np.random.seed)
• for learning algorithms, for example to verify that they don't get stuck in local optima:
  • test stability of one optimal solution:
    • start from optimal solution
    • start from little perturbation of optimal solution
  • generate data from the model with known parameters and recover the parameters