geot 0.1.0

Optimal Transport metrics for evaluating spatiotemporal predictions

GEOT: A spatially explicit framework for evaluating spatio-temporal predictions

This repo contains code to evaluate spatiotemporal predictions with Optimal Transport (OT). In contrast to standard evaluation metrics (MSE, MAE etc), the OT error is a spatial metric that evaluates the spatial distribution of the errors.

Install

First, install a suitable torch version. Then, install the package via pypi:

pip install geot

Or from source:

git clone https://github.com/mie-lab/geospatialOT.git
cd geospatialOT
conda create -n geot_env
conda activate geot_env
pip install .

Explanation of usage:

• Assume we want to predict some observations in several locations, e.g., bike sharing demand at bike sharing stations, over time
• Usually, the error is just averaged over locations (MSE between GT and prediction)
• However, in real-world applications, there are distance-based costs involved with prediction errors. For example, prediction errors cause costs for relocating bikes
• Assue we can define a cost matrix with the pairwise costs between locations, indicating the cost to account for errors
• With Optimal Transport, we can compute the minimum costs for transforming the predictions to the ground truth - a better indicator for the real-world costs of prediction errors than just the MSE

With this code, you can compute the OT error for your predictions. The input is usually just the observations at a set of location, the predictions and the cost matrix. The output is the OT error (a single number) or the optimal transport matrix T.

Tutorial

Check out our tutorial to get started with a simple example.