Unit Testing ============ As our code continues to grow, how can we be sure it is working as expected? If we make minor changes to the code, what tests can we run to make sure we didn't break anything? Are our functions written well enough to capture and correctly handle all of the edge cases we throw at them? In this module, we will use the Python ``pytest`` library to write unit tests: small tests that are designed to test specific individual components of code. After working through this module, students should be able to: * Find the documentation for the Python ``pytest`` library * Identify parts of code that should be tested and appropriate assert methods * Write and run reasonable unit tests Getting Started --------------- Unit tests are designed to test small components (e.g. individual functions) of your code. They should demonstrate that things that are expected to work actually do work, and things that are expected to break raise appropriate errors. The Python ``pytest`` unit testing framework supports test automation, set up and shut down code for tests, and aggregation of tests into collections. It is not part of the Python Standard Library, so we must install it. .. code-block:: console [login-coe332]$ pip3 install --user pytest Find the `documentation here `_. Pull a copy of the `meteorite landings data analysis script `_ we have been working on, and a copy of the `meteorite landings json file `_, if you don't have copies already. Devise a Reasonable Test ------------------------ The functions in this Python3 script are relatively simple, but how can we be sure they are working as intended? Let's begin with the ``compute_average_mass()`` function. We might choose to test it manually using the Python3 interactive interpreter: .. code-block:: python3 >>> from ml_data_analysis import compute_average_mass >>> >>> data = [{'thing': 1}, {'thing': 2}] >>> >>> print(compute_average_mass(data, 'thing')) 1.5 So simple! We import our code, hand-craft a simple data structure, and send the data plus the key we are interested in to our function. We know off the top of our heads that the average of 1 and 2 is 1.5, and that is in fact the number we get back. Instead of writing that out each time we want to test, let's instead put this into another Python3 script. When writing test scripts, it is a common convention to name them the same name as the script you are testing, but with the ``test_`` prefix added at the beginning. .. code-block:: console [login-coe332]$ ls Meteorite_Landings.json ml_data_analysis.py [login-coe332]$ touch test_ml_data_analysis.py [login-coe332]$ ls Meteorite_Landings.json ml_data_analysis.py test_ml_data_analysis.py Open up the script with VIM and put in our testing code from before: .. code-block:: python3 :linenos: from ml_data_analysis import compute_average_mass data = [{'thing': 1}, {'thing': 2}] print(compute_average_mass(data, 'thing')) Next try to execute the test script on the command line: .. code-block:: console [login-coe332]$ python3 test_ml_data_analysis.py 1.5 Great! We assume the test is working. But we still have to look at the output (1.5) and remember back to our hand-crafted data and make sure that is the correct result. It would be more efficient if we had a way to check that the correct answer is returned in our test script itself. To do this, we can use the ``assert`` statement. .. code-block:: python3 :linenos: :emphasize-lines: 4 from ml_data_analysis import compute_average_mass data = [{'thing': 1}, {'thing': 2}] assert(compute_average_mass(data, 'thing') == 1.5) Now instead of printing the result, we use ``assert`` to make sure it is equal to our expected outcome. If the conditional is true, nothing will be printed. If the conditional is false, we will see an ``AssertionError``. EXERCISE ~~~~~~~~ * Write a few more tests to convince yourself that the function is in fact returning the average of the input values. * Modify one of the tests so that it should fail, and execute the tests to confirm that it does fail. * If you have multiple tests that pass and multiple tests that fail, how would you know? Automate Testing with Pytest ---------------------------- Pytest is an excellent framework for small unit tests and for large functional tests (as we will see later in the semester). If you previously installed pytest with pip3, now would be a good time to double check that the installation worked and there is an executable called ``pytest`` in your PATH: .. code-block:: console [login-coe332]$ pytest --version pytest 7.0.0 Next, we just need to make a minor organizational change to our test code. Pytest will automatically look in our working tree for files that start with the ``test_`` prefix, and execute the tests within. .. code-block:: python3 :linenos: :emphasize-lines: 3 from ml_data_analysis import compute_average_mass def test_compute_average_mass(): assert compute_average_mass([{'a': 1}, {'a': 2}], 'a') == 1.5 assert compute_average_mass([{'a': 1}, {'a': 2}, {'a': 3}], 'a') == 2 assert compute_average_mass([{'a': 10}, {'a': 1}, {'a': 1}], 'a') == 4 Call the ``pytest`` executable in your top directory, it will find your test function in your test script, run that function, and finally print some informative output: .. code-block:: console ==================================== test session starts ==================================== platform linux -- Python 3.6.8, pytest-7.0.0, pluggy-1.0.0 rootdir: /home/wallen/coe-332/code-organization collected 1 item test_ml_data_analysis.py . [100%] ===================================== 1 passed in 0.01s ===================================== What Else Should We Test? ------------------------- The simple tests we wrote above seem almost trivial, but they are actually great sanity tests to tell us that our code is working. What other behaviors of our ``compute_average_mass()`` function should we test? In no particular order, we could test the following non-exhaustive list: * If the list only contains one dictionary object, the function still behaves as expected * The return value should be type ``float`` * If we send it an empty list, that should raise some sort of exception * If we send it a list of non-uniform dictionaries (e.g. the dictionaries don't all have the expected key), we should get a ``KeyError`` * If we send it bad values (e.g. a value is a string instead of an expected float), we should get a ``ValueError`` * If we send it a string that doesn't appear in the dictionaries, we should get a ``KeyError`` .. tip:: A list of all of the built-in Python3 exceptions can be found in the `Python docs `_. To test some of these behaviors, let's create some additional assertions and organize them into their own functions. .. code-block:: python3 :linenos: :emphasize-lines: 11 from ml_data_analysis import compute_average_mass import pytest def test_compute_average_mass(): assert compute_average_mass([{'a': 1}], 'a') == 1 assert compute_average_mass([{'a': 1}, {'a': 2}], 'a') == 1.5 assert compute_average_mass([{'a': 1}, {'a': 2}, {'a': 3}], 'a') == 2 assert compute_average_mass([{'a': 10}, {'a': 1}, {'a': 1}], 'a') == 4 assert isinstance(compute_average_mass([{'a': 1}, {'a': 2}], 'a'), float) == True def test_compute_average_mass_exceptions(): with pytest.raises(ZeroDivisionError): compute_average_mass([], 'a') # send an empty list with pytest.raises(KeyError): compute_average_mass([{'a': 1}, {'b': 1}], 'a') # dictionaries not uniform with pytest.raises(ValueError): compute_average_mass([{'a': 1}, {'a': 'x'}], 'a') # value not a float with pytest.raises(KeyError): compute_average_mass([{'a': 1}, {'a': 2}], 'b') # key not in dicts After adding the above tests, run ``pytest`` again: .. code-block:: console ==================================== test session starts ==================================== platform linux -- Python 3.6.8, pytest-7.0.0, pluggy-1.0.0 rootdir: /home/wallen/coe-332/code-organization collected 2 items test_ml_data_analysis.py .. [100%] ===================================== 2 passed in 0.01s ===================================== Success! The tests for our first function are passing. Our test suite essentially documents our intent for the behavior of the ``compute_average_mass()`` function. And, if ever we change the code in that function, we can see if the behavior we intend still passes the test. EXERCISE ~~~~~~~~ In the same test script, but under new test function definitions: * Write tests for the ``check_hemisphere()`` function * Write tests for the ``count_classes()`` function Additional Resources -------------------- * `Pytest documentation `_. * `Exceptions in Python `_