mlskeleton

A Python package that generates a folder structure for machine learning/deep learning projects.

Installation

To install the package, run the following command:

pip install mlskeleton

Usage

To generate the folder structure, run the following command:

mlskeleton /path/to/root/folder

This will create the following folder structure at the specified root folder path:

project_name/
|
|- data/
|   |- raw/
|   |   |- raw_data_file_1.csv
|   |   |- raw_data_file_2.csv
|   |   ...
|   |
|   |- processed/
|   |   |- processed_data_file_1.csv
|   |   |- processed_data_file_2.csv
|   |   ...
|
|- models/
|   |- model_1.pkl
|   |- model_2.pkl
|   ...
|
|- notebooks/
|   |- data_exploration.ipynb
|   |- model_training.ipynb
|   |- model_evaluation.ipynb
|   ...
|
|- src/
|   |- data/
|   |   |- data_processing.py
|   |   |- data_cleaning.py
|   |   ...
|   |
|   |- features/
|   |   |- feature_extraction.py
|   |   |- feature_selection.py
|   |   ...
|   |
|   |- models/
|   |   |- model_training.py
|   |   |- model_evaluation.py
|   |   ...
|   |
|   |- visualization/
|   |   |- visualizations.py
|   |   ...
|   |
|   |- utils/
|   |   |- utils.py
|   |   ...
|
|   |- tests/
|   |   |- test_data.py
|   |   |- test_features.py
|   |   |- test_visualization.py
|   |   |- test_models.py
|   |   |- test_utils.py
|   |   ...
|
|-- reports/
|   |-- figures/
|   |-- presentations/
|   |-- papers/
|
|- .gitignore
|- requirements.txt
|- README.md

Explanation:

• data: This folder should contain all the data required for the project, both raw and processed. It is a good idea to keep the raw data separate from the processed data to make it clear which data has been transformed in some way and how.
• models: This folder should contain the trained machine learning models, saved in a format that allows them to be easily loaded and used (e.g. using the pickle library in Python).
• notebooks: This folder should contain Jupyter notebooks used for data exploration, model training, model evaluation, and any other analysis.
• src: This folder should contain the source code for the project, organized into subfolders for data processing,features, model training and evaluation, visualization, etc.
  • src/data/: This folder should contain code for loading and interacting with the data.
  • src/features/: This folder should contain code for generating features from the data.
  • src/models/: This folder should contain code for building and training machine learning models.
  • src/visualization/: This folder should contain code for creating visualizations of the data and model performance.
  • src/utils/: This folder should contain utility code that is used by other parts of the project.
  • tests/: This folder should contain test scripts or modules for testing the code in the src/ directory.
• reports/: This folder should contain any figures, presentations, or papers that are created as part of the project.
• .gitignore: This file should contain a list of file and folder names that should be ignored by Git (e.g. large data files that should not be committed to the repository).
• requirements.txt: This file should contain a list of the libraries and packages required to run the code in the project, so that they can be easily installed by someone else who wants to run the code.
• README.md: This file should contain a brief description of the project and instructions for how to set up and run the code.

Contributing

If you want to contribute to the package, please follow the guidelines in the CONTRIBUTING.md file.

License

The package is licensed under the MIT License. See the LICENSE file for more information.