luma-ml 1.1.9

A Comprehensive Python Module for Machine Learning and Data Science

A Comprehensive Python Module for Machine Learning and Data Science

About

Luma is a comprehensive, user-friendly Python library designed for both beginners and advanced users in the field of machine learning and data science. It provides a wide range of tools and functionalities to streamline the process of data analysis, model building, evaluation, and deployment.

Purpose

Luma is built for an educational purpose, focused on implementing various machine learning algorithms and models from scratch solely depending on low-level libraries such as NumPy.

Key Features

• Easy Data Handling: Simplify data preprocessing, transformation, and visualization.
• Model Building: Access a variety of machine learning algorithms and models.
• Model Evaluation: Utilize robust tools for model validation and tuning.

Packages

Name	Description
luma.classifier	Toolkit for classification models including various algorithms.
luma.clustering	Focuses on unsupervised learning and clustering algorithms.
luma.core	Foundational backbone providing essential data structures and utilities.
luma.ensemble	Ensemble learning methods for improved model performance.
luma.extension	Various extensions for Luma development. Not for end-users.
luma.interface	Protocols and custom data types for internal use within Luma.
luma.metric	Performance metrics for evaluating machine learning models.
luma.migrate	Import and export of machine learning models within Luma.
luma.model_selection	Tools for model selection and hyperparameter tuning.
luma.neural 🔗	Deep learning models and neural network utilities. A dedicated DL package for Luma.
luma.pipe	Creating and managing machine learning pipelines.
luma.preprocessing	Data preprocessing functions for machine learning tasks.
luma.reduction	Dimensionality reduction techniques for high-dimensional datasets.
luma.regressor	Comprehensive range of regression algorithms.
luma.visual	Tools for model visualization and data plotting.

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Getting Started

Installation

To get started with Luma, install the package using pip:

pip install luma-ml

Or for a specific version,

pip install luma-ml==[any_version]

Import

After installation, import Luma in your Python script to access its features:

import luma

Acceleration

Luma supports MLX based NumPy acceleration on Apple Silicon. By importing Luma’s neural package, it will automatically detect Apple’s Metal Performance Shader(MPS) availability and directly apply MLX acceleration for all execution flows and operations using luma.neural.

import luma.neural

Otherwise, the default CPU based operation is applied.

For more details, please refer to the link 🔗 shown at Luma’s neural package description.

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Others

Contribution

Luma is an open-source project, and we welcome contributions from the community. 😃

Whether you're interested in fixing bugs, adding new features, or improving documentation, your help is appreciated.

License

Luma is released under the GPL-3.0 License. See LICENSE file for more details.

Inspired By

Luma is inspired by these libraries:

Specifications

	Description
Latest Version	1.1.9
Lines of Code	~37.2K
Dependencies	NumPy, SciPy, Pandas, Matplotlib, Seaborn, MLX(Optional)