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. |
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.
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 | 1.2.5 |
| Lines of Code | ~40.1K |
| Dependencies | NumPy, SciPy, Pandas, Matplotlib, Seaborn, MLX(Optional) |
2 maintainers
Metadata
2 maintainers
Release files for luma-ml 1.2.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| luma_ml-1.2.5.tar.gz | 195.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| luma_ml-1.2.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 447.3 kB
Release files / luma_ml-1.2.5.tar.gz
| Download URL | luma_ml-1.2.5.tar.gz |
|---|---|
| Size | 195.3 kB |
| Tags | Source |
|
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Release files / luma_ml-1.2.5-py3-none-any.whl
| Download URL | luma_ml-1.2.5-py3-none-any.whl |
|---|---|
| Size | 252.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/5.1.1 CPython/3.12.1
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