As part of our goal of providing more frequent releases, we are excited to announce VW 9.2.0! Some highlights include a large restructuring of VW’s library structure which better organizes related source and header files, the ability to call learn and predict directly from a string in java, access to model weights in Python, and colored output logs for errors, warnings, and general info.
Historically VW has clumped together most of the source and headers files for its reductions into one directory. As more features and targets were added, it seemed necessary to better organize this structure so that new and current users could compartmentalize different parts of VW. Check out this PR for more details.
You can now predict and learn directly from a string in Java with the
Model weights can now be accessed directly in Python in JSON format using the
Both reductions have gone through major revamps to improve their statistical accuracy.
Logistic regression can now take non-binary costs via mean field implementation. E.g. for loss
[0,1] the update is
p*update(1) + (1-p)*update(-1).
Logs from VW’s command line interface will now display errors in red, warnings in yellow, and info in green.
A huge thank you and welcome to all of the new contributors since the last release:
And of course thank you to existing contributors: