MLuno: Univariate Regression Python Package

Overview

MLuno is a Python package designed for performing univariate regression tasks in machine learning. The package provides tools to simulate data, train models, make predictions, calculate metrics, and visualize results. The focus is on simplicity and clarity, allowing users to perform regression analysis from scratch using minimal dependencies.

Features

• Data Simulation: Generate synthetic data suitable for regression tasks.
• Data Splitting: Easily split data into training and testing sets.
• Regression Models: Implement basic univariate regression models.
• Prediction: Make predictions with trained models.
• Metrics Calculation: Evaluate model performance using common metrics.
• Visualization: Plot regression results and visualize model performance.

Getting Started

Prerequisites

Ensure you have Python installed. You can install the required dependencies with:

pip install -r requirements.txt

Installation

To install the package, navigate to the root directory of the project and run:

pip install .

Usage

You can start using MLuno by importing the package and utilizing its modules:

import mluno

# Example usage:
data = mluno.data.make_line_data()
model = mluno.regressors.LinearRegressor()
model.fit(data['X_train'], data['y_train'])
predictions = model.predict(data['X_test'])
mluno.plot.plot_predictions(data['X_test'], predictions)

Directory Structure

Your project directory should look like this:

./mluno/
│
├── src/
│   │
│   └── mluno/
│       ├── __init__.py
│       ├── conformal.py
│       ├── data.py
│       ├── metrics.py
│       ├── plot.py
│       └── regressors.py
│
├── tests/
│   ├── test_conformal.py
│   ├── test_data.py
│   ├── test_metrics.py
│   ├── test_plot.py
│   └── test_regressors.py
│
├── _quarto.yml
├── .gitignore
├── .python-version
├── index.qmd
├── pyproject.toml
└── README.md

• src/mluno/: Contains the source code for the package.
• tests/: Contains unit tests for each module.
• _quarto.yml, index.qmd: Files for generating documentation with Quarto.
• pyproject.toml: Defines package dependencies and build system.
• .gitignore: Specifies files and directories to be ignored by Git.

Modules

• data.py: Functions for data generation and splitting.
• regressors.py: Implementations of various regression models.
• metrics.py: Functions for calculating performance metrics.
• plot.py: Visualization tools for regression results.
• conformal.py: (Optional) Implements split conformal prediction.

Documentation

MLuno includes comprehensive documentation generated using quartodoc and quarto. Every function and class is documented with numpydoc style docstrings.

To build the documentation, run:

rye run quartodoc build
quarto preview

Testing

Unit tests are provided to ensure the correctness of the package. Tests can be run using pytest:

rye test

License

This project is open-source and available under the MIT License.