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.
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.
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:
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
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
There is documentation, and a simple tutorial.
FAQs
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.
We found that feature-clock demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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