Aggrigator 🐊

Aggrigator is a lightweight Python library for uncertainty aggregation in deep learning workflows.
Whether you're working with segmentation maps or just want to summarize per-pixel uncertainties — Aggrigator gives you a powerful and flexible toolbox to make sense of it all.

With a clean API and built-in strategies, you can easily:

• Reduce pixelwise uncertainty maps to scalar scores for evaluation or ranking.
• Apply patch-based, class-specific, or thresholded aggregation.
• Incorporate spatial correlation metrics like Moran's I or Geary’s C.
• Compare strategies side-by-side with summaries and plots.

Designed to be modular, explainable, and research-friendly.
Use it out of the box, or extend it with your own aggregation logic!

Installation

To install the aggrigator, clone the repository and navigate inside the directory, run the following command:

pip install aggrigator

now you can import the library in your python code with:

import aggrigator

Try it out yourself

Check out the interactive example_notebook.ipynb to see Aggrigator in action.
You’ll learn how to:

• ✅ Generate and visualize uncertainty maps.
• ⚙️ Apply and compare aggregation strategies.
• 🧠 Use class-aware masks for targeted aggregation.