The Python DataFrame-to-VW effort has been growing, the latest available changes beeing:
cb_adf code paths have been consolidated into using the
cb_adf path, without however issuing any breaking changes (#2680). cb examples are internally translated to
cb_adf examples. This behaviour can be turned off by supplying the
--cb_force_legacy flag when
--cb N is used. The motivation behind this change is so that the
cb reduction can benefit from the work done on
cb_adf which has gotten more attention over the years
Using the wildcard (
:) when doing cubic (
--cubic :::) and higher order (
--interactions ::::) interactions has been significantly sped up (#2993). This optimization now affects cubics and higher order interactions
A new flag has been added that allows the limiting of the log output of VW (#3021). By using
--limit_output <N> a hard limit
N can be set to the total printed lines. This does not include vw progressive validation loss output which will remain unaffected
Upcoming deprecations can now be tracked here.
Deprecation warnings will be added in minor releases and the warnings will state the major release in which they will take effect.
Deprecation warnings for this release added to:
Experimental support for getting internal VW metrics has been added (#2959) and can be enabled using the
--extra_metrics <output_file> argument. Currently richer information exists for reductions that use
cb_explore (see example output for ccb_explore_adf here). The resulting json schema is still very fluid and is expected to change. Users that build and use VW from source can add their own metrics if they wish to do so. The metrics are also made available via the Python API (see here for a usage example) where they can be accessed via a Python dictionary
Learners can now be built using templated builders (#2918) and reductions builders are slowly being transitioned to use the new builders.
A huge thank you and welcome to all of the new contributors since the last release:
And of course thank you to existing contributors: