Working on a machine learning project is more than just training a model. It involves various steps like Data Gathering, Data Preprocessing, Model Building, and Model Evaluation. End-to-end Machine Learning pipelines are built to cover each step. Testing code at each level is important to ensure the proper working of each step.
In the SDLC (software development lifecycle), testing plays an important role. Now since machine learning projects also follow a development life cycle, testing has become crucial in machine learning projects as well.
In this article, we will discuss how testing is useful for building an effective machine-learning system. By the end of this article, you will know about various types of testing and how they can be included in machine learning pipelines.
Why is Testing Required in Machine Learning Projects?
Including test cases in the machine learning code may seem unimportant in the initial stage of development. But it only becomes necessary at a later stage when the annoying bugs break the system apart. Therefore, testing should be considered an important part in the early stage of any development cycle so that we can decrease downstream costs and wasted time.
Once we have designed our test cases, we can automatically execute them every time we change or add anything to our codebase to ensure the correct workflow.
But testing machine learning systems is much more challenging as compared to conventional software tests.
- In traditional software systems, we write the logic for how users can interact with the software. The test cases are used to validate whether the written logic is working as expected or not.
- In machine learning systems, we expect a desired behaviour and train model to produce the logic for it. We need to write test cases to validate if the learned logic will consistently produce the desired behaviour.
Types of Tests
Below are the types of tests that can be used at different points in the development cycle.
- Unit Tests: Tests on individual components each having a particular responsibility.
- Integration Tests: Tests on combined functionality of individual components.
- Acceptance Tests: Tests to verify that requirements have been met, usually referred to as User Acceptance Test (UAT).
- System Tests: Tests on the design of the system.
- Regression Tests: Tests based on errors that are seen before and making sure that new changes don’t reintroduce them.
Testing Methodology
The framework to use when composing tests is the Arrange Act Assert Methodology.
- Arrange: The first step is to set up the different inputs to test on.
- Act: Apply the inputs to the component we want to test.
- Assert: Confirm that we have received the expected output.
Types of Unit Tests in Machine Learning Systems
Below are a few types of unit tests that can be used to perform testing in machine learning. The idea is to test machine learning artifacts (data, code and model).
- Data Level Testing
- Code Level Testing
- Model Level Testing
1. Data Level Testing
The data level unit test is used to test the validity of the data generated at different points of the pipeline.
We can use libraries like deep checks and great expectations to test if the data is as per expectations. These libraries allow us to create expectations as to what our data should look like in a standardized way.
2. Code Level Testing
The code level unit testing is used to test if each individual component of code is returning the expected output. It is done using a dummy input file if the code returns the expected output.
We can use libraries like pytest as our testing framework for its powerful features such as parameterization, fixture, marker and more. It can also generate status and coverage reports.
The status report represents the status (success or failure of each function).
The coverage report represents the percentage of code covered in test cases. We want to ensure 100% coverage of our codebase. We can also exclude lines that we don’t want to cover and maintain.
3. Model Level Testing
Model level unit tests are used to test machine learning models.
For example
- In the case where we are reading the model object (pickle file), model level testing can be used to validate if the model object is as expected.
- Model level testing can be used to test if the predictions by model object is as expected.
Difference Between Model Evaluation and Testing
In model evaluation, the performance of the machine learning model is evaluated using different types of metrics and presented in terms of the metric report and plots whereas testing machine learning code refers to checking if the model is behaving as expected.
Tools For Machine Learning Testing
Below are a few python libraries that can be used for performing testing in machine learning systems:
Benefits of Machine Learning System Testing
- Ensure correct working of machine learning pipelines.
- Reduce overall cost and improve the quality of the system.
- Better reporting capability.
Challenges in Machine Learning Testing
- We need to make sure that the project not only works locally and in production. But it continues to work correctly in production.
- As we know, data is an important part of machine learning projects. We need to pay special attention to data testing.
Conclusion
Thank you for reading this article till the end, I hope you are convinced to include testing in your next machine learning project. I promise it is definitely going to improve the overall efficiency of your project.
Feel free to ask any query or give your feedback in the comment box below.
Happy Learning 🙂
