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Notebook and Script Naming Conventions in ML Projects

Overview

Properly naming Jupyter notebooks and scripts is essential for quick identification, efficient management, and collaborative ease in machine learning projects. A systematic naming convention helps in understanding the file's purpose at a glance and tracking its evolution over time.

Importance of Naming

A well-defined naming convention is crucial for organizing and managing files in any ML project.

Naming Convention Structure

Use the following format for naming notebooks and scripts:

<type>_<topic>_<version>_<YYYYMMDD>.<extension>

Components:

  • Type: A short identifier indicating the nature of the work (e.g., eda for exploratory data analysis, preprocess for data preprocessing, model for model training).
  • Topic: A concise descriptor of the notebook's or script's main focus.
  • Version: An optional version number or identifier, especially useful if the notebook or script undergoes significant iterative updates.
  • Date: The creation or last modified date in YYYYMMDD format.
  • Extension: The file extension, like .ipynb for Jupyter notebooks, .py for Python scripts.

Components Breakdown

Understanding each component of the naming convention helps in creating more informative and easily recognizable file names.

Guidelines:

  1. Descriptive and Purposeful:
  2. Start with a type that categorizes the file based on its primary purpose in the ML workflow.

  3. The topic should be sufficiently descriptive to convey the specific focus or task of the notebook/script.

  4. Versioning:

  5. Include a version number if the file is part of an iterative process, such as v1, v2, or more detailed semantic versioning like 1.0, 1.1.

  6. Date Stamp:

  7. Adding the date (in YYYYMMDD format) helps in identifying the most recent version or understanding the timeline of development.

  8. Consistency:

  9. Maintain a consistent naming convention across all notebooks and scripts for ease of organization and retrieval.

  10. Clarity and Brevity:

  11. Ensure the name is clear yet concise. Avoid overly long names but provide enough information to understand the file's content and purpose.

Examples:

  • eda_customer_segmentation_v1_20240101.ipynb
  • preprocess_data_cleaning_v2_20240215.py
  • model_train_regression_20240310.ipynb

Naming Examples

These examples illustrate how the naming convention is applied in practice.

Conclusion

This naming convention for Jupyter notebooks and scripts will foster a more organized and manageable ML project environment. It aids in quickly locating specific files, understanding their purpose, and tracking their evolution over time.