| Name | luma-ml JSON |
| Version |
1.2.3
JSON |
| download |
| home_page | https://github.com/ChanLumerico/luma |
| Summary | A Comprehensive Python Module for Machine Learning and Data Science |
| upload_time | 2024-10-15 11:09:20 |
| maintainer | None |
| docs_url | None |
| author | ChanLumerico |
| requires_python | >=3.12 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
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No coveralls.
|
<img src="https://raw.githubusercontent.com/ChanLumerico/luma/main/img/title/dark.png" alt="logo" height="50%" width="50%">
A Comprehensive Python Module for Machine Learning and Data Science
<img alt="pypi-version" src="https://img.shields.io/pypi/v/luma-ml?logo=python&logoColor=white&color=blue">
<img alt="pypi-downloads" src="https://img.shields.io/pypi/dm/luma-ml">
<img src="https://img.shields.io/badge/total downloads-13.2k-red">
<img alt="GitHub code size in bytes" src="https://img.shields.io/github/languages/code-size/ChanLumerico/luma?color=yellow">
<img alt="Code Style" src="https://img.shields.io/badge/code%20style-black-000000.svg">
## 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` [🔗](https://github.com/ChanLumerico/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`:
```bash
pip install luma-ml
```
Or for a specific version,
```bash
pip install luma-ml==[any_version]
```
### Import
After installation, import Luma in your Python script to access its features:
```python
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`.
```python
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:
<img src="https://skillicons.dev/icons?i=sklearn,pytorch,tensorflow">
### Specifications
| | Description |
| --- | --- |
| Latest Version | 1.2.3 |
| Lines of Code | ~39.3K |
| Dependencies | NumPy, SciPy, Pandas, Matplotlib, Seaborn, MLX(Optional) |
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"description": "<img src=\"https://raw.githubusercontent.com/ChanLumerico/luma/main/img/title/dark.png\" alt=\"logo\" height=\"50%\" width=\"50%\">\n\nA Comprehensive Python Module for Machine Learning and Data Science\n\n<img alt=\"pypi-version\" src=\"https://img.shields.io/pypi/v/luma-ml?logo=python&logoColor=white&color=blue\">\n<img alt=\"pypi-downloads\" src=\"https://img.shields.io/pypi/dm/luma-ml\">\n<img src=\"https://img.shields.io/badge/total downloads-13.2k-red\">\n<img alt=\"GitHub code size in bytes\" src=\"https://img.shields.io/github/languages/code-size/ChanLumerico/luma?color=yellow\">\n<img alt=\"Code Style\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\">\n\n## About\n\nLuma is a comprehensive, user-friendly Python library designed for both beginners\nand advanced users in the field of machine learning and data science. It provides\na wide range of tools and functionalities to streamline the process of data analysis,\nmodel building, evaluation, and deployment.\n\n### Purpose\n\nLuma 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`.\n\n### Key Features\n\n- **Easy Data Handling**: Simplify data preprocessing, transformation, and visualization.\n- **Model Building**: Access a variety of machine learning algorithms and models.\n- **Model Evaluation**: Utilize robust tools for model validation and tuning.\n\n## Packages\n\n| Name | Description |\n| --- | --- |\n| `luma.classifier` | Toolkit for classification models including various algorithms. |\n| `luma.clustering` | Focuses on unsupervised learning and clustering algorithms. |\n| `luma.core` | Foundational backbone providing essential data structures and utilities. |\n| `luma.ensemble` | Ensemble learning methods for improved model performance. |\n| `luma.extension` | Various extensions for Luma development. Not for end-users. |\n| `luma.interface` | Protocols and custom data types for internal use within Luma. |\n| `luma.metric` | Performance metrics for evaluating machine learning models. |\n| `luma.migrate` | Import and export of machine learning models within Luma. |\n| `luma.model_selection` | Tools for model selection and hyperparameter tuning. |\n| `luma.neural` [\ud83d\udd17](https://github.com/ChanLumerico/luma-neural) | Deep learning models and neural network utilities. A dedicated DL package for Luma. |\n| `luma.pipe` | Creating and managing machine learning pipelines. |\n| `luma.preprocessing` | Data preprocessing functions for machine learning tasks. |\n| `luma.reduction` | Dimensionality reduction techniques for high-dimensional datasets. |\n| `luma.regressor` | Comprehensive range of regression algorithms. |\n| `luma.visual` | Tools for model visualization and data plotting. |\n\n---\n\n## Getting Started\n\n### Installation\n\nTo get started with Luma, install the package using `pip`:\n\n```bash\npip install luma-ml\n```\n\nOr for a specific version,\n\n```bash\npip install luma-ml==[any_version]\n```\n\n### Import\n\nAfter installation, import Luma in your Python script to access its features:\n\n```python\nimport luma\n```\n\n### Acceleration\n\nLuma supports `MLX` based `NumPy` acceleration on **Apple Silicon**. By importing Luma\u2019s neural package, it will automatically detect Apple\u2019s Metal Performance Shader(MPS) availability and directly apply MLX acceleration for all execution flows and operations using `luma.neural`.\n\n```python\nimport luma.neural\n```\n\nOtherwise, the default CPU based operation is applied.\n\nFor more details, please refer to the link \ud83d\udd17 shown at Luma\u2019s neural package description.\n\n---\n\n## Others\n\n### Contribution\n\nLuma is an open-source project, and we welcome contributions from the community. \ud83d\ude03\n\nWhether you're interested in fixing bugs, adding new features, or improving documentation, your help is appreciated.\n\n### License\n\nLuma is released under the GPL-3.0 License. See `LICENSE` file for more details.\n\n### Inspired By\n\nLuma is inspired by these libraries:\n\n<img src=\"https://skillicons.dev/icons?i=sklearn,pytorch,tensorflow\">\n\n### Specifications\n\n| | Description |\n| --- | --- |\n| Latest Version | 1.2.3 |\n| Lines of Code | ~39.3K |\n| Dependencies | NumPy, SciPy, Pandas, Matplotlib, Seaborn, MLX(Optional) |\n",
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