How Should You Test Your Machine Learning Project? A Beginner’s Guide

🌈 Abstract

The article discusses the importance of testing in machine learning projects, which is often neglected. It provides a guide on how to test various components of a machine learning pipeline, focusing on fine-tuning BERT for text classification on the IMDb dataset and using the industry-standard libraries like pytest and pytest-cov.

🙋 Q&A

[01] Introduction to Testing in Machine Learning

1. What is the main purpose of the article? The main purpose of the article is to introduce the essentials of testing various parts of a machine learning pipeline, using the example of fine-tuning BERT for text classification on the IMDb dataset.

2. What are the key components of the project structure? The key components of the project structure are:

• src/: Contains the main files for loading datasets, training and evaluating models.
• tests/: Contains different Python scripts, with one test file for each script in the src/ folder.
• conftest.py: Contains configuration information about the tests, including fixtures.
• models/: Contains the fine-tuned BERT model.
• environment.yml and requirements.txt: Contain the necessary dependencies.

3. What is the purpose of the conftest.py file? The conftest.py file is not a testing function per se, but it contains some configuration information about the tests, especially fixtures, which are used to set up some context or state before running tests and to clean up afterward.

[02] Testing at Different Levels of Granularity

1. What are the three levels of granularity for testing discussed in the article? The three levels of granularity for testing discussed in the article are:

1. Testing a single function
2. Testing a script
3. Testing the full codebase

2. How does the article demonstrate testing a single function? The article demonstrates testing a single function by writing a test for the clean_text() function in the test_data_loader.py file.

3. How does the article demonstrate testing a script? The article demonstrates testing a script by adding a tokenize_text() function to the data_loader.py script and writing a test for it in the test_data_loader.py file.

4. How does the article demonstrate testing the full codebase? The article demonstrates testing the full codebase by having a similar structure of test files for each script in the src/ folder, and using fixtures defined in the conftest.py file to manage test dependencies.

[03] Measuring Test Coverage

1. What is the purpose of measuring test coverage? The purpose of measuring test coverage is to identify which parts of the code need more testing, as a high coverage percentage does not necessarily mean the code is bug-free.

2. How does the article demonstrate measuring test coverage? The article demonstrates measuring test coverage using the pytest-cov plugin, which generates a coverage report. The report shows the coverage percentage for each file in the codebase, which can be used to identify areas that need more testing.

3. What are the key takeaways from the coverage report shown in the article? The key takeaways from the coverage report are:

• The main.py file has 0% coverage, indicating that it needs to be tested.
• The evaluation.py file has only 19% coverage, suggesting that more tests should be written for this file.