testing-for-datascience

A small repo to get familiar with testing in datascience

We are on our way to industrialize our product. In order to do so, I have to improve our ability to test our code resistance to bugs & errors.

Testing will allow use to

• deploy in production with more confidence
• anticipate potential errors in productive runs
• ensure a code maintenance in a continuous improvement loop
• improve our code readability

Contents

• What is software testing ?
• Unit testing vs. Integration testing
• Architecture
• Simple test case
• Instant Running
• Interpreting the results
• Configuration
• Markers
• Advanced commands
• Resources
• See also

What is software testing ?

Software testing is a system of check-ups to ensure that the output from our code matches with the expected results

---

Unit testing vs. Integration testing

Unit Testing	Integration Testing
Unit testing is a type of testing to check if the small piece of code is doing what it is suppose to do.	Integration testing is a type of testing to check if different pieces of the modules are working together.
The scope of Unit testing is narrow, it covers the Unit or small piece of code under test. Therefore while writing a unit test shorter codes are used that target just a single class.	The scope of Integration testing is wide, it covers the whole application under test and it requires much more effort to put together.
Unit testing checks a single component of an application.	Integration testing spands over multiple components

---

For this, we'll be using the pytest framework.

Architecture

Adding a tests suite in a project requires :

• a new tests/ folder at the project root
• pytest.ini file at the project root to configure the tests suite
• .py files inside tests/ bearing the test functions

Typical file structure is the following :

├── main.py             <- The top-level README for developers using this project.
├── ...
├── src 
    ├── __init__.py     <- Makes src a Python module
│   ├── ...
│   ...
|
├── pytest.ini          <- Pytest configuration file
|
└── tests               <- Test folder
    ├── data            <- Datasets for testings
    │   ├── data.csv    <- Test dataset
    |   └── ...
    ├── test_X.py       <- Test script
    └── test_Y.py       <- Another test script

Simple test case

Let's take a (really) simple python function.

To create a simple test suite, we'll have to set up the skeleton project :

├── src 
│   └── functions.py
└── tests
    └── test_function.py

functions.py will contain the fairly simple function :

def maxinlist(input_list):
    return max(input_list)

And test_function.py will contain :

from src.functions import maxinlist

def test_maxinlist_returns_max():
    input_list = [1, 4, 125, 94, 843, 42]
    list_max = 843
    assert maxinlist(input_list) == list_max

The test script first import the function to test.

It then checks if the function maxinlist indeed returns the max value in the list. Note that input and output are manually defined in the test function. A test systematically ends with an assert and does not "returns" a result.

Running python3 -m pytest will return :

======================================== test session starts ========================================
platform darwin -- Python 3.7.3, pytest-5.3.2, py-1.8.1, pluggy-0.13.1
rootdir: /private/tmp/pytest_test
collected 1 item

tests/test_function.py .                                                                      [100%]

========================================= 1 passed in 0.04s =========================================

It indeed indicates that :

• 1 test was collected
• 100% was found tests were ran in the test_function.py script
• The test was a success as we can wee with the .

If we change the test as following :

from src.functions import maxinlist

def test_maxinlist_returns_max():
    input_list = [1, 4, 125, 94, 843, 42]
    list_max = 26
    assert maxinlist(input_list) == list_max

... it would yield the following result :

======================================== test session starts ========================================
platform darwin -- Python 3.7.3, pytest-5.3.2, py-1.8.1, pluggy-0.13.1
rootdir: /private/tmp/pytest_test
collected 1 item

tests/test_function.py F                                                                      [100%]

============================================= FAILURES ==============================================
____________________________________ test_maxinlist_returns_max _____________________________________

    def test_maxinlist_returns_max():
        input_list = [1, 4, 125, 94, 843, 42]
        list_max = 26
>       assert maxinlist(input_list) == list_max
E       assert 843 == 26
E        +  where 843 = maxinlist([1, 4, 125, 94, 843, 42])

tests/test_function.py:6: AssertionError
========================================= 1 failed in 0.07s =========================================

As we can see :

• 1 test was collected
• 100% of the collected tests were ran
• The test has however failed as we can see with the F failed indication

The debug tool allows us to see that the test experienced an AssertionError on the statement

assert 843 == 26

Instant running

You can run the test suite from the project root using the following command :

$ python3 -m pytest

Notes :

• the -m argument allows Python to run with a preloaded module (in this case pytest)
• pytest is based on built-in module unittest. You might have to conda install pytest or pip install pytest to install the module

Interpreting the results

If you execute the previous command without modifying the repo, you should get the following result :

$ python3 -m pytest
============================================= test session starts ======================================================
platform darwin -- Python 3.7.5, pytest-5.3.2, py-1.8.0, pluggy-0.13.1
rootdir: /Users/ben/PycharmProjects/testing-for-datascience, inifile: pytest.ini
collected 4 items                                                                                                       

tests/test_featuresbuilding.py ..F.                                                                               [100%]

================================================== FAILURES ============================================================
_____________________________ boundToFailTests.test_returns_asserterror_if_int_input ___________________________________
...

rootdir: /Users/ben/PycharmProjects/testing-for-datascience, inifile: pytest.ini

This line indicates where the pytest suite is executed. Be sure that it is the project root ! If a configuration file is present, it should appear here (inifile: pytest.ini)

collected 4 items 

This indicates that 4 tests has been found.
Important note : pytest automatically & recursively search for tests (see "Configuration" section for more information) in subfolders. That is why executing pytest from the project root is important
Keeping all test in the tests/ folder is a good practice.

tests/test_featuresbuilding.py ..F.                                                                               [100%]

This is the important line. It indicates which tests were passed with success and which ones failed.

• a dot . indicates a successful test
• a F indicates a failed test

The [100%] indicates that all tests were executed (with a success or failed status)

================================================== FAILURES ============================================================
_____________________________ boundToFailTests.test_returns_asserterror_if_int_input ___________________________________
...

This section was voluntarily truncated for readability

All information below FAILURES returns information regarding failed test. This section allows the developer to debug the tests one by one.

Configuration

We mentioned earlier that pytest recursively search for tests in the code. The mechanism is defined by the pytest.ini configuration file at the project folder. If no configuration file is set up, pytest will behave as if the configuration was the following :

[pytest]
python_classes = *Tests
python_functions = test_*
python_files = test_*

• pytest will look for file with naming test_* throughout all the subfolders
• within these files, it will look for test classes with naming *Tests
• within the same files, it will look for test functions with naming test_* (hence our example test_maxinlist_returns_max)

Note : this is the convention for naming test objects

Note : test function names should be self-explanatory for code readability. Don't hesitate to get verbose !

Note : Test classes are just a way to group test functions and apply specific logics to them (see "Makers" section)

Markers

Complete test suite can take some time to execute, and for productivity purposes, a developer might want to run only a limited group of tests. Moreover, when a commit is done on a project, the developer might want to test only the impacted portion of the project (for example : if a feature impacting the Data Collection step in a Data Science project is about to be released, you might want ot run only the tests related to this portion of the pipeline)
Markers are "tags" for test functions or classes.
This allows to categorize tests when executing a test suite.

Markers must be defined in the pytest.ini file :

[pytest]
python_classes = *Tests
python_functions = test_*
python_files = test_*

markers =
    data_ingestion
    feature_engineering
    machine_learning
    data_exposition

To stamp one or several markers to a class or function, it must be set as a fixture.

from pytest import mark
from src.functions import maxinlist

@mark.feature_engineering
def test_maxinlist_returns_max():
    input_list = [1, 4, 125, 94, 843, 42]
    list_max = 843
    assert maxinlist(input_list) == list_max

Note that mark has to be imported from pytest package. In the example above, the maxslopeDetectionTests class and all subsequent functions are marked with the marker feature_engineering.

The main advantage of markers relies in selecting testing while running a test suite :

$ python3 -m pytest -m "feature_engineering"
======================================== test session starts ========================================
platform darwin -- Python 3.7.3, pytest-5.3.2, py-1.8.1, pluggy-0.13.1
rootdir: /private/tmp/pytest_test, inifile: pytest.ini
collected 1 item

tests/test_function.py .                                                                      [100%]

========================================= 1 passed in 0.04s =========================================

Note : if feature_engineering is not a referenced marker in pytest.ini, you'll get the following warning :

========================================= warnings summary ==========================================
/usr/local/lib/python3.7/site-packages/_pytest/mark/structures.py:327
  /usr/local/lib/python3.7/site-packages/_pytest/mark/structures.py:327:PytestUnknownMarkWarning: Unknown pytest.mark.first_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/latest/mark.html
    PytestUnknownMarkWarning,

-- Docs: https://docs.pytest.org/en/latest/warnings.html
=================================== 1 passed, 1 warning in 0.04s ====================================

Markers syntax

You can use trickier combinations of markers in your command :

$ python3 -m pytest -m "not feature_engineering"
$ python3 -m pytest -m "data_ingestion and not feature_engineering"
$ python3 -m pytest -m "feature_engineering or machine_learning"

See this page for more examples.

Advanced commands

You find below more commands to interact with pytest

Verbose mode (useful for debugging)

$ python3 -m pytest -v

Help

$ python3 -m pytest -h

Passing variables in command (see this page for usage)

$ python3 -m pytest --variable=value

Getting detailed report on skipped tests (see this page)

$ python3 -m pytest -rs

Try only tests contained in a single file

$ python3 -m pytest test_function.py

Test only a certain class in a certain file

$ python3 -m pytest test_function.py::ClassTests

Test only a certain unit test

$ python3 -m pytest test_function.py::ClassTests::test_function

Run pytest quietly (less verbose)

$ python3 -m pytest -q

---

Resources

• Pytest Documentation
• gutfeeling's Github repo

See also

• Pytest .html reports
• Fixtures allows you to reduce code cumbersomeness
• Elegant Automation Frameworks with Python and Pytest - Udemy Course - ~3-4 hours of training
• Unit Testing for Data Science in Python - Datacamp Course - 4-5 hours of training