feature-clock 1.0.2
Feature Clock, provides visualizations that eliminate the need for multiple plots to inspect the influence of original variables in the latent space. Feature Clock enhances the explainability and compactness of visualizations of embedded data.
Requires Python: >=3.11

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# Feature Clock: High-Dimensional Effects in Two-Dimensional Plots

- [Package](https://pypi.org/project/feature-clock/) 
- [Documentation](https://github.com/OlgaOvcharenko/feature_clock_visualization/blob/main/tutorials/docs.md) 
- [Tutorial](https://github.com/OlgaOvcharenko/feature_clock_visualization/blob/main/tutorials/iris.md) 
- [Examples](https://github.com/OlgaOvcharenko/feature_clock_visualization/tree/main/test) 

[![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/pylint-dev/pylint)

## What is it?

It is difficult for humans to perceive high-dimensional data. Therefore, high-dimensional data is projected into lower dimensions to visualize it. 
Many applications benefit from complex nonlinear dimensionality reduction techniques (e.g., UMAP, t-SNE, PHATE, and autoencoders), but the effects of individual high-dimensional features are hard to explain in the latent spaces. 
Most solutions use multiple two-dimensional plots to analyze the effect of every variable in the embedded space, but this is not scalable, leading to k plots for k different variables. 
Our solution, **Feature Clock**, provides novel visualizations that eliminate the need for multiple plots to inspect the influence of original variables in the latent space. **Feature Clock** enhances the explainability and compactness of visualizations of embedded data.

## Table of Contents

- [Main Features](#main-features)
- [Where to get it](#where-to-get-it)
- [Installation](#instalation)
- [License](#license)
- [Documentation](#documentation)

## Main Features
**Feature Clock** allows creation of three types of static visualizations, highlighting the contributions of the high-dimensional features to linear directions of the two-dimensional spaces produced by nonlinear dimensionality reduction: 

- ***Global* Feature Clock** indicating the direction of features’ contributions in low-dimensional space for the whole dataset.
- ***Local* Feature Clock** explaining features’ impact within selected points.
- ***Inter-group* Feature Clock** visualizing contributions between groups of points. 

## Where to get it
The source code is currently hosted on GitHub at:
https://github.com/OlgaOvcharenko/feature_clock_visualization.git

Binary installers for the latest released version are available at the [Python
Package Index (PyPI)](https://pypi.org/project/feature-clock/).

```sh
# PyPI
pip install feature-clock
```

## Instalation
Feature Clock can be installed from PyPI:

```sh
pip install feature-clock
```

All dependencies are listed in [requirements.txt](https://github.com/OlgaOvcharenko/feature_clock_visualization/blob/main/requirements.txt) and can be installed separately.

```sh
pip install -r requirements.txt
```

## License
[Apache License Version 2.0](LICENSE)

## Documentation
There is [documentation](https://github.com/OlgaOvcharenko/feature_clock_visualization/blob/main/tutorials/docs.md), and a simple [tutorial](https://github.com/OlgaOvcharenko/feature_clock_visualization/blob/main/tutorials/iris.md).


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