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

• Package
• Documentation
• Tutorial
• Examples

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
• Where to get it
• Installation
• License
• 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).

# PyPI
pip install feature-clock

Instalation

Feature Clock can be installed from PyPI:

pip install feature-clock

All dependencies are listed in requirements.txt and can be installed separately.

pip install -r requirements.txt

License

Apache License Version 2.0

Documentation

There is documentation, and a simple tutorial.

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