Diffprivlib 0.6.5

This release of diffprivlib is a minor update to fix bugs that have resulted in the release of Numpy v2.0 and Scikit-Learn v1.5.

What's Changed

• [MNT] Prepare for Numpy 2.0 and Scikit-Learn 1.5 by @naoise-h in #92

Full Changelog: 0.6.4...0.6.5

Diffprivlib 0.6.4

This release of diffprivlib is a minor update to fix a bug in some tools and to maintain compatibility with the newly-released Scikit-Learn v1.4.

Fixed

• Minor bugs arising from the relase of Scikit-Learn v1.4
• Incorrect bounds calculation in Nan-supporting statistical functions in the tools module (#87)

Added

• Support for Python 3.12

Merged Pull Requests

• [DOC] Updating documentation by @naoise-h in #89
• [BUG] Fix bounds calculation in nanmean, etc. by @naoise-h in #90
• [BUG] Fixes for scikit-learn v1.4 by @naoise-h in #91

Full Changelog: 0.6.3...0.6.4

Diffprivlib 0.6.3

This release of diffprivlib is a minor update to maintain compatibility with the newly-released Scikit-Learn v1.3.

Fixed

• Bug in DecisionTreeClassifier and RandomForestClassifier arising from the release of Scikit-Learn v1.3

Merged Pull Requests

• Update readme adding known publications by @stefano81 in #78
• [BUG] Fixes _Node class for scikit-learn v1.3 by @naoise-h in #86
• [BUG] Fix random_state bug in quantile by @naoise-h in #85

Full Changelog: 0.6.2...0.6.3

Diffprivlib 0.6.2

This release of diffprivlib is a minor update on 0.6.1 to maintain compatibility with the newly-released Scikit-learn 1.2, while also adding explicit support for Python 3.11.

Added

• Support for Python 3.11

Fixed

• Minor changes to support Scikit-learn's new parameter validation
• Minor changes to PCA and RandomForestClassifier resulting from recent scikit-learn deprecations

Merged Pull Requests

• [MNT] Diffprivlib 0.6.2 by @naoise-h in #77

Full Changelog: 0.6.1...0.6.2

Diffprivlib 0.6.1

This release is a minor bugfix to support the upcoming release of Numpy 1.24.0

Fixed

• Numpy's histogramdd has deprecated the normed keyword, which has now been removed from diffprivlib.

Merged Pull Requests

• [MNT] diffprivlib 0.6.1 by @naoise-h in #75

Full Changelog: 0.6.0...0.6.1

Diffprivlib 0.6.0

This version of diffprivlib supports Python versions 3.8 to 3.10.

Changed

Breaking:

• RandomForestClassifier has been substantially rewritten to fix previously-reported bugs and to improve performance. It now takes a bounds parameter (similar to other models) instead of the previous feature_domains parameter (#70)

Non-breaking:

• All diffprivlib models and tools now accept a random_state parameter to specify a seed for the random number generator (RNG), or to specify a RandomState instance to be used. The implementation follows closely that of scikit-learn, and more details can be found in the PR #72.
• To overcome difficulties with its installation on Windows, crlibm is now an optional dependency. (#67)

Merged Pull Requests

• [BUG] Making crlibm an optional dependency by @naoise-h in #67
• [ENH] Refactor RandomForestClassifier by @naoise-h in #70
• [ENH] Add random state support to diffprivlib by @naoise-h in #72
• [MNT] Diffprivlib 0.6 by @naoise-h in #74

Full Changelog: 0.5.2...0.6.0

Diffprivlib 0.5.2

This is a minor release of diffprivlib that adds the snapping mechanism and fixes bugs introduced by Scikit-learn 1.1.0.

Added

• Snapping mechanism, Snapping (#46, by @danrr)

Fixed

• Bugs in LinearRegression, LogisticRegression and RandomForestClassifier introduced by release of Scikit-learn 1.1.0 (#65)

New Contributors

• @danrr made their first contribution in #46

Full Changelog: 0.5.1...0.5.2

Diffprivlib 0.5.1

This release of diffprivlib is a minor update on 0.5, adding explicit support for Python 3.10.

Added

• Support for Python 3.10 (#61)

Full Changelog: 0.5.0...0.5.1

Diffprivlib 0.5.0

This release of diffprivlib features a number of additions, fixes and enhancements. This version of diffprivlib supports Python version 3.7 to 3.9.

Added

• A differentially private random forest classifier, RandomForestClassifier, has been added to the models module (#41, by mismayil)
• The Exponential and PermuteAndFlip mechanisms now support monotonic utility functions as an input parameter (08c4d98)
• Templates have been added for issues and pull requests (#52, by stefano81)
• We are transitioning to Github Actions for tests and other checks (#54)

Changed

Breaking:

• Mirroring the equivalent behaviour in scikit-learn (since version 0.23), most parameters for diffprivlib models must now be specified as keyword arguments. Failing to do so will raise a TypeError. (5fa781e)

Non-breaking:

• The Laplace, Gaussian, Vector and Bingham distributions are now securely sampled to prevent privacy leakage due to their floating point representation (#47)
• Diffprivlib now requires scikit-learn 0.23, numpy 1.19, and scipy 1.5, or later (#51)

Fixed

• Bug in quantile for uniform data fixed (#43, d5ac459)
• Optimised upper bound for var (#45, by justanotherlad)
• Bug in Exponential mechanism (#50, by justanotherlad)
• histogramdd and histogram2d now prioritise bin edges from bins over range. (96ef33c)

Removed

• The Wishart mechanism has been removed (#42, eeae0d7)

Diffprivlib 0.4.1

This release of diffprivlib is a minor update to v0.4 to fix a bug in models.LinearRegression.

Fixed

• Bug in models.LinearRegression which incorrectly calculated the sensitivity for the objective (#35)