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@tennlee tennlee released this 07 Dec 03:23
2.0.0
90d1c2f
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Learn about vigilant mode.

Release Notes (What's New)

Version 2.0.0 (December 7, 2024)

For a list of all changes in this release, see the full changelog. Below are the changes we think users may wish to be aware of.

Breaking Changes

  • The function scores.probability.tw_crps_for_ensemble previously took an optional (mis-spelled) argument chainging_func_kwargs. The spelling has been corrected and the argument is now chaining_func_kwargs. See PR #780 and PR #772.
  • For those who develop on scores, you will need to update your installation of the scores package with pip install -e .[all], to get updated versions of black, pylint and mypy. See PR #768, PR #769 and PR #771.

Features

  • Added three new metrics:
    • Brier score for ensembles: scores.probability.brier_score_for_ensemble. See PR #735.
    • Negative predictive value: scores.categorical.BasicContingencyManager.negative_predictive_value. See PR #759.
    • Positive predictive value: scores.categorical.BasicContingencyManager.positive_predictive_value. See PR #761 and PR #756.
  • Also added one new emerging metric and two supporting functions:
    • Risk matrix score: scores.emerging.risk_matrix_scores.
    • Risk matrix score - matrix weights to array: scores.emerging.matrix_weights_to_array.
    • Risk matrix score - warning scaling to weight array: scores.emerging.weights_from_warning_scaling.
      See PR #724 and PR #794.
  • A new method called format_table was added to the class BasicContingencyManager to improve visualisation of 2x2 contingency tables. The tutorial Binary_Contingency_Scores was updated to demonstrate the use of this function. See PR #775.
  • The functions scores.processing.comparative_discretise, scores.processing.binary_discretise and scores.processing.binary_discretise_proportion now accept either a string indicating the choice of operator to be used, or an operator from the Python core library operator module. Using one of the operators from the Python core module is recommended, as doing so is more reliable for a variety of reasons. Support for the use of a string may be removed in future. See PR #740 and PR #758.

Documentation

  • Added "The Risk Matrix Score" tutorial. See PR #724 and PR #794.
  • Updated the "Brier Score" tutorial to include a new section about the Brier score for ensembles. See PR #735.
  • Updated the "Binary Categorical Scores and Binary Contingency Tables (Confusion Matrices)"
    tutorial:
    • Included "positive predictive value" in the list of binary categorical scores.
    • Included "negative predictive value" in the list of binary categorical scores.
    • Demonstrated the use of the new format_table method for visualising 2x2 contingency tables.
      See PR #759 and PR #775.
  • Updated the “Contributing Guide”:
    • Added a new section: "Creating Your Own Fork of scores for the First Time".
    • Updated the section: "Workflow for Submitting Pull Requests".
    • Added a new section: "Pull Request Etiquette".
      See PR #787.
  • Updated the README:
    • Added a link to a video of a PyCon AU 2024 conference presentation about scores. See PR #783.
    • Added a link to the archives of scores on Zenodo. See PR #784.
  • Added Scoringrules to "Related Works". See PR #746, PR #766 and PR #789.

Internal Changes

  • Removed scikit-learn as a dependency. scores has replaced the use of scikit-learn with a similar function from SciPy (which was an existing scores dependency). This change was manually tested and found to be faster. See PR #774.
  • Version pinning of dependencies in release files (the wheel and sdist files used by PyPI and conda-forge) is now managed and set by the hatch_build script. This allows development versions to be free-floating, while being more specific about dependencies in releases. The previous process also aimed to do this, but was error-prone. A new entry called pinned_dependencies was added to pyproject.toml to specify the release dependencies. See PR #760.

Contributors to this Release

Arshia Sharma* (@arshiaar), A.J. Fisher* (@AJTheDataGuy), Liam Bluett* (@lbluett), Jinghan Fu* (@JinghanFu), Sam Bishop* (@techdragon), Robert J. Taggart (@rob-taggart), Tennessee Leeuwenburg (@tennlee), Stephanie Chong (@Steph-Chong) and Nicholas Loveday (@nicholasloveday).

* indicates that this release contains their first contribution to scores.

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