How do you write Machine Learning code that's easy to document?

1 Use consistent and descriptive naming

One of the simplest and most effective ways to document your code is to use consistent and descriptive naming for your variables, functions, classes, and modules. This will help you and others to quickly identify what each component does, how it relates to others, and what inputs and outputs it expects. For example, instead of using vague names like x, y, or data, use more specific names like features, labels, or train_data. Similarly, instead of using generic names like model, function, or script, use more informative names like linear_regression, preprocess_data, or train_model.

2 Add comments and docstrings

Another way to document your code is to add comments and docstrings that explain the purpose, logic, and functionality of your code. Comments are short notes that you can write within your code to clarify or justify certain decisions, assumptions, or steps. Docstrings are longer descriptions that you can write at the beginning of your functions, classes, or modules to provide a summary, parameters, return values, and examples. Both comments and docstrings can help you and others to understand the context and intention of your code, as well as to avoid errors or bugs. For example, you can use comments to explain why you chose a certain hyperparameter or algorithm, and use docstrings to describe what your function does and how to use it.

3 Follow style and formatting guidelines

A third way to document your code is to follow style and formatting guidelines that make your code more consistent, organized, and readable. Style and formatting guidelines are conventions or rules that define how to structure, write, and present your code. They can cover aspects such as indentation, spacing, line length, naming, punctuation, and syntax. Following these guidelines can help you and others to avoid confusion, errors, or conflicts when working with your code. For example, you can use tools like PEP 8 for Python, Google Style Guide for R, or TensorFlow Style Guide for TensorFlow to check and improve your code style and format.

4 Use version control and logging

A fourth way to document your code is to use version control and logging systems that track and record the changes and progress of your code. Version control systems are tools that allow you to save, manage, and compare different versions of your code, as well as to collaborate with others. Logging systems are tools that allow you to capture, store, and analyze the output and performance of your code, such as metrics, errors, or warnings. Using these systems can help you and others to document the history and evolution of your code, as well as to debug and optimize it. For example, you can use tools like Git for version control, and tools like TensorBoard or MLflow for logging.

5 Generate automatic documentation

A fifth way to document your code is to generate automatic documentation from your comments and docstrings. Automatic documentation is a process that extracts and converts the information and format of your comments and docstrings into a more user-friendly and accessible format, such as HTML, PDF, or Markdown. Generating automatic documentation can help you and others to view, share, and update your documentation more easily and efficiently. For example, you can use tools like Sphinx for Python, Roxygen2 for R, or Pydoc for TensorFlow to create and manage automatic documentation.

6 Use existing frameworks and tools

A sixth way to document your code is to use existing frameworks and tools that provide built-in documentation features or support. Existing frameworks and tools are software packages or libraries that offer predefined functions, classes, or modules that you can use to implement your machine learning code. Using these frameworks and tools can help you and others to document your code by leveraging their existing documentation, examples, and tutorials. For example, you can use frameworks like Scikit-learn, Keras, or PyTorch to write machine learning code that follows standard conventions and practices, and use tools like Jupyter Notebook, R Markdown, or Colab to write machine learning code that integrates documentation with code execution.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?