scoringrules documentation

scoringrules documentation#



scoringrules is a lightweight python library that provides scoring rules to evaluate probabilistic forecasts.

It’s original goal was to reproduce the functionality of the R package scoringRules in python, thereby allowing forecasting practitioners working in python to enjoy the same tools as those working in R. The methods implemented in scoringrules are therefore based around those available in scoringRules, which are rooted in the scientific literature on probabilistic forecasting.

The scoring rules available in scoringrules include, but are not limited to, the

  • Brier Score

  • Logarithmic Score

  • (Discrete) Ranked Probability Score

  • Continuous Ranked Probability Score (CRPS)

  • Dawid-Sebastiani Score

  • Energy Score

  • Variogram Score

  • Gaussian Kernel Score

  • Threshold-Weighted CRPS

  • Threshold-Weighted Energy Score

  • Quantile Score

  • Interval Score

Features#

  • Fast computation of several probabilistic univariate and multivariate verification metrics

  • Multiple backends: support for numpy (accelerated with numba), jax and pytorch

  • Didactic approach to probabilistic forecast evaluation through clear code and documentation

Installation#

Requires python >=3.11!

pip install scoringrules

Documentation#

Learn more about scoringrules in its official documentation at https://scoringrules.readthedocs.io/en/latest/.

Quick example#

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)

Citation#

If you found this library useful, 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}
}

Acknowledgements#

  • The widely-used R package scoringRules served as a reference for this library, which greatly facilitated our work. We are additionally grateful for fruitful discussions with the authors.

  • The quality of this library has also benefited a lot from discussions with (and contributions from) Nick Loveday and Tennessee Leeuwenburg, whose python library scores similarly provides a comprehensive collection of forecast evaluation methods.