Metadata-Version: 2.3
Name: wordviz
Version: 0.6.0
Summary: A lightweight Python package for loading, analyzing, and visualizing word embeddings.
License: MIT
Author: Elena Zen
Author-email: elenazen563@gmail.com
Requires-Python: >=3.11,<3.14
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Provides-Extra: encoding
Provides-Extra: viz
Requires-Dist: adjusttext (>=1.3.0,<2.0.0)
Requires-Dist: gensim (>=4.4.0,<5.0.0)
Requires-Dist: matplotlib (>=3.10.1,<4.0.0)
Requires-Dist: numpy (>=1.18.5,<2.4)
Requires-Dist: plotly (>=6.0.1,<7.0.0)
Requires-Dist: scikit-learn (>=1.6.1,<2.0.0)
Requires-Dist: scipy (>=1.14.1,<2.0.0)
Requires-Dist: seaborn (>=0.13.2,<0.14.0)
Requires-Dist: sentence-transformers (>=2.0.0) ; extra == "encoding"
Requires-Dist: torch (>=1.12.0) ; extra == "encoding"
Requires-Dist: transformers (>=4.20.0) ; extra == "encoding"
Requires-Dist: umap-learn (>=0.5.7,<0.6.0) ; extra == "viz"
Description-Content-Type: text/markdown

[![PyPI version](https://img.shields.io/pypi/v/wordviz.svg)](https://pypi.org/project/wordviz/)
[![Python](https://img.shields.io/pypi/pyversions/wordviz.svg)](https://pypi.org/project/wordviz/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

![logo](https://raw.githubusercontent.com/elena563/wordviz/master/images/logo.png)

**WordViz** is a Python visualization library designed for exploring and visualizing word embeddings. Built on top of popular libraries such as `matplotlib`, `plotly`, and `gensim`, WordViz provides intuitive tools for analyzing embeddings through clustering, similarity exploration, and dimensionality reduction, all wrapped in interactive and customizable plots.
With WordViz, users can gain insights into the structure of their word embeddings, making it a valuable tool for researchers and practitioners in natural language processing.

This project was created as part of my Bachelor's Degree thesis in Statistics and Information Management with title (translated): "Word Embeddings in Practice: Designing a Library for Visualization and Operations"

**version 0.6.0**

Documentation: https://wordviz.readthedocs.io/

Built with:  
  
[![Python](https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white)](https://www.python.org/)
[![Plotly](https://img.shields.io/badge/Plotly-7A76FF?style=flat&logo=plotly&logoColor=white)]()
[![matplotlib](https://img.shields.io/badge/matplotlib-15557C?style=flat)]()
[![scikit-learn](https://img.shields.io/badge/scikit--learn-f7931e?style=flat&logo=scikitlearn&logoColor=white)]()
[![Gensim](https://img.shields.io/badge/Gensim-8199F7?style=flat)]()
[![NumPy](https://img.shields.io/badge/NumPy-013243?style=flat&logo=numpy&logoColor=white)]()

## Last Version Updates

### Added
- Added HDBSCAN and Gaussian Mixture Model (GMM) clustering methods to the `plot_clusters` function.
- New `metric` parameter in `plot_clusters` to select the distance metric for clustering, with default values based on the chosen method.
- New `ReducedCache` class to manage reduced embeddings for both 2D and 3D visualizations, allowing for more efficient caching, validation and retrieval of reduced embeddings.
- Added `custom` parameter for `red_method` to use embeddings reduced by external methods, with validation of shape and dimensionality. 

### Fixed
- Better error handling in the clustering functions.
- Removed automatic dimensionality reduction before clustering, reducing coupling between clustering and reduction.

### Updated
- `compute_distances` now can handle distances between a target embedding and a set of embeddings, reducing complexity from n^3 to n^2. A benchmark test was added to validate the performance improvement.
- `map_colors` has now a fallback to a default color palette if the number of unique labels exceeds the available colors in the selected theme, ensuring that all labels are assigned a different color.
- `plot_dendrogram` now supports coloring by cluster if a `n_clusters` to visualize is provided. Together with the threshold line, now all the branches are colored according to the cluster they belong to. 

See more about previous changes in [CHANGELOG.md](CHANGELOG.md)


## Main Features

- Load and explore pretrained embeddings (e.g., GloVe, FastText)
- Select from a variety of available embeddings
- Visualize embeddings in 2D or 3D with flexible dimensionality reduction options
- Identify and plot the most similar words to a given token
- Visualize clusters of related words
- Interactive plots powered by `plotly`
- Support for many light and dark themes


## Installation

Install the latest version from PyPI:

```bash
pip install wordviz
```

### Notes: Python version compatibility

The only versions that support Python 3.13 are the latest releases starting from version 0.3.5.

If you still have problems with the installation, it is recommended to use Python 3.12.

```bash
uv init --python 3.12
```

This warning will be removed in the next versions of wordviz, as Python 3.13 will be fully supported.


## Usage

You can load and manage embeddings though the `EmbeddingLoader` class, and then visualize them with the `Visualizer` (or `Visualizer3D`) class.

```python
from wordviz import EmbeddingLoader, Visualizer

loader = EmbeddingLoader()
loader.load_from_file('path/to/your/embedding/file', 'word2vec')

vis = Visualizer(loader)
vis.plot_embeddings()
```

You can explore all functionalities through the example notebook provided in the `docs/` folder:

👉 [View example notebook](docs/example.ipynb)


## Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request


## License

This project is licensed under the MIT License.


## Contacts

Elena Zen - [My Portfolio Website](https://elenazen.it/en) - info.elenazen@gmail.com
