scores: Verification and Evaluation for Forecasts and Models

A list of over 50 metrics, statistical techniques and data processing tools contained in scores is available here.

scores is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, scores primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities.

Documentation is hosted at scores.readthedocs.io.
Source code is hosted at github.com/nci/scores.
The tutorial gallery is hosted at as part of the documentation, here.

Overview

Here is a curated selection of the metrics, tools and statistical tests included in scores:

scores not only includes common scores (e.g. MAE, RMSE), it includes novel scores not commonly found elsewhere (e.g. FIRM, Flip-Flop Index), complex scores (e.g. threshold weighted CRPS), and statistical tests (such as the Diebold Mariano test). Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). scores provides its own implementations where relevant to avoid extensive dependencies.

scores primarily supports xarray datatypes for Earth system data allowing it to work with NetCDF4, HDF5, Zarr and GRIB data sources among others. scores uses Dask for scaling and performance. Some metrics work with pandas and we aim to expand this capability.

All of the scores and metrics in this package have undergone a thorough scientific review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice.

Contributing

To find out more about contributing, see our Contributing Guide.

All interactions in discussions, issues, emails and code (e.g. pull requests, code comments) will be managed according to the expectations outlined in the code of conduct and in accordance with all relevant laws and obligations. This project is an inclusive, respectful and open project with high standards for respectful behaviour and language. The code of conduct is the Contributor Covenant, adopted by over 40,000 open source projects. Any concerns will be dealt with fairly and respectfully, with the processes described in the code of conduct.

Using This Package

The installation guide describes four different use cases for installing, using and working with this package.

Most users currently want the all installation option. This includes the mathematical functions (scores, metrics, statistical tests etc.), the tutorial notebooks and development libraries.

From a Local Checkout of the Git Repository

> pip install -e .[all]

Here is a short example of the use of scores:

> import scores
> forecast = scores.sample_data.simple_forecast()
> observed = scores.sample_data.simple_observations()
> mean_absolute_error = scores.continuous.mae(forecast, observed)
> print(mean_absolute_error)
<xarray.DataArray ()>
array(2.)

To install the mathematical functions ONLY (no tutorial notebooks, no developer libraries), use the minimal installation option. minimal is a stable version with limited dependencies and can be installed from the Python Package Index.

> pip install scores

Finding, Downloading and Working With Data

All metrics, statistical techniques and data processing tools in scores work with xarray. Some metrics work with pandas. As such, scores works with any data source for which xarray or pandas can be used. See the Data Sources page and this tutorial for more information on finding, downloading and working with different sources of data.

Acknowledging This Work

If you use scores for a published work, we would appreciate you citing our arXiv preprint:

Leeuwenburg, T., Loveday, N., Ebert, E. E., Cook, H., Khanarmuei, M., Taggart, R. J., Ramanathan, N., Carroll, M., Chong, S., Griffiths, A., & Sharples, J. (2024). scores: A Python package for verifying and evaluating models and predictions with xarray. arXiv. https://doi.org/10.48550/arXiv.2406.07817