Scoringrules is a python library for evaluating probabilistic forecasts by computing scoring rules and other diagnostic quantities. It aims to assist forecasting practitioners by providing a set of tools based the scientific literature and via its didactic approach.
- Fast computations of several probabilistic univariate and multivariate verification metrics
- Multiple backends: support for numpy (accelerated with numba), jax, pytorch and tensorflow
- Didactic approach to probabilistic forecast evaluation through clear code and documentation
Requires python >=3.10!
pip install scoringrules
Learn more about scoringrules in its official documentation at https://frazane.github.io/scoringrules/.
import scoringrules as sr
import numpy as np
obs = np.random.randn(100)
fct = obs[:,None] + np.random.randn(100, 21) * 0.1
sr.crps_ensemble(obs, fct)- Brier Score
- Continuous Ranked Probability Score (CRPS)
- Logarithmic score
- Error Spread Score
- Energy Score
- Variogram Score
If you found this library useful for your own research, consider citing:
@software{zanetta_scoringrules_2024,
author = {Francesco Zanetta and Sam Allen},
title = {Scoringrules: a python library for probabilistic forecast evaluation},
year = {2024},
url = {https://github.com/frazane/scoringrules}
}
scoringRules served as a reference for this library. The authors did an outstanding work which greatly facilitated ours. The implementation of the ensemble-based metrics as jit-compiled numpy generalized ufuncs was first proposed in properscoring, released under Apache License, Version 2.0.