ContextLab/hypertools

What it solves

HyperTools simplifies the process of visualizing high-dimensional data by providing a streamlined pipeline to reduce dimensionality and create plots in a single function call. It helps researchers gain geometric insights into complex datasets that are otherwise impossible to visualize directly.

How it works

The library integrates several scientific Python tools (matplotlib, scikit-learn, seaborn) to process high-dimensional datasets. It allows users to apply a sequence of manipulations—such as normalization, dimensionality reduction (via PCA, UMAP, or Autoencoders), alignment, and clustering—and then render the results as 2D or 3D plots. It supports both static and interactive backends (Plotly) and can handle various data sources including Hugging Face datasets and LSL streams.

Who it’s for

It is designed for data scientists, ML researchers, and analysts who need to explore the structure, trajectories, and clusters of high-dimensional data visually.

Highlights

• Unified Pipeline: Combines manipulation, reduction, alignment, and clustering into a single hyp.plot call.
• Advanced Visualization: Supports 3D hull surfaces, density shading, and various animation styles (morphing, spinning, and sliding windows).
• Flexible Reduction: Includes standard methods like PCA and UMAP, as well as torch-backed autoencoders (Variational, Sparse, Convolutional, etc.).
• Text Integration: Built-in support for text vectorizers like Word2Vec and Doc2Vec via gensim.
• Predictive Tools: Includes forecasting and imputation capabilities using Kalman filters, Gaussian Processes, and Hugging Face's Chronos.