Releases: jjfiv/fastrank
Fastrank 0.8 (M1 support, Python 3.7)
We revamped how CI scripts work and upgraded Maturin in order to get "Apple Silicon" support.
What's Changed
- Update zstd requirement from 0.4 to 0.9 by @dependabot-preview in #45
- Upgrade to GitHub-native Dependabot by @dependabot-preview in #42
- Update zstd requirement from 0.9 to 0.10 by @dependabot in #48
- Update ordered-float requirement from 2.0 to 3.0 by @dependabot in #50
- Update zstd requirement from 0.10 to 0.11 by @dependabot in #49
- Update zstd requirement from 0.11 to 0.12 by @dependabot in #51
- Try CI.yml modified from cramjam project by @jjfiv in #54
Full Changelog: 0.7.0...0.8.0
The 'predict_scores' release
- CModel now has
predict_scores
that returns a sparse representation ofDict[int, float]
where the position in the arrays you've loaded correspond to the score. - We also have
predict_dense_scores
which returns aList[float]
with the same semantics. If you have subsampled queries, this may make less sense than the aforementioned method. - We have some better testing covering these features.
- Note: trying again because of a glitch in automatic releases ('fix' for #32 insufficient)
0.6.1: move to manylinux2010
Rather than require the arbitrary libc etc. from the gh-actions publish, align with manylinux2010. Fixed some documentation nits along the way.
0.6.0: Windows, fast-float and python3.6
Updates in 0.6.0 are:
- Minimum python version now 3.6 -- 3.5 started failing on CI, so it's gone now.
- support for faster float parsing -- on my machine the msn30k dataset took 90s to load, and now only takes 60s. Thanks rust libraries!
- Windows supported - now that I have regular access to a windows machine, I will make sure PyPI has windows builds.
First public alpha: 0.4
FastRank
FastRank
is an implementation of CoordinateAscent
[1] from Ranklib that you can pip install; written in Rust and uses threads for efficiency; it will scale much better than the Java version to large datasets and many features.
It also has RandomForests
, and someday LambdaMART
(others depending on interest). I've been thinking a lot about what the limits of coordinate ascent are (linearity), and will probably play with that in future versions.
This is ready for production use in the sense that I used it for my TREC submission this year. The python API could use some thoughts and experiences (post issues on Github).
pip install fastrank
[1] Metzler, D., & Croft, W. B. (2007). Linear feature-based models for information retrieval. Information Retrieval, 10(3), 257-274.