dr-diagnostics 0.3.3

Diagnostics for dimensionality reduction methods such as t-SNE and UMAP

dr-diagnostics

dr-diagnostics is a Python package for visual diagnostics of dimensionality reduction embeddings.

It helps users inspect how well a low-dimensional embedding preserves structure from the original high-dimensional data. The package supports user-provided embeddings, as well as built-in workflows for t-SNE and UMAP.

Tutorial

A hands-on tutorial notebook walks through the package on a synthetic dataset (two clusters of different density plus a lone outlier) and shows how the diagnostics reveal density equalisation and outlier absorption:

Installation

Install from PyPI:

pip install dr-diagnostics

Inside a Jupyter notebook or Google Colab cell, prefix the command with !:

!pip install dr-diagnostics

Features

• Visualize original data and low-dimensional embeddings, with optional named class legends.
• Compare high-dimensional and low-dimensional Euclidean distances (distance-fit plots).
• Approximate geodesic distances using a k-nearest-neighbour graph and compare them against low-dimensional Euclidean distances.
• Compute rank matrices from pairwise distance matrices and compare high-dimensional ranks with low-dimensional ranks.
• Trustworthiness and continuity curves over a range of neighbourhood sizes.
• Compare t-SNE similarity matrices P and Q, and UMAP similarity matrices V and W.
• Generate a six-panel dashboard combining the diagnostics in one figure.
• Inspect a single selected point and its k-nearest neighbours across all panels, or a specific pair of points (with automatic detection of the most distorted pairs).
• Show the true 3D scatter of the original data when it is three-dimensional (e.g. the Swiss Roll).

License

This project is licensed under the MIT License.