Adding support for new frameworks
New frameworks added since 1.5:
- AutoXGBoost
- MLJar-supervised
- MLPlan
Upgraded existing frameworks versions.
Improved frameworks version management
In most cases, users can try an older or a more recent version of a given framework, simply by creating a local framework definition with the version they want to use (https://github.com/openml/automlbenchmark/blob/master/docs/HOWTO.md#framework-definition), and force the framework setup (python runbenchmark.py my_framework -s force).
Run on OpenML suites and tasks directly
Specify benchmark as openml/s/X or openml/t/Y to run on a suite or task, e.g.: python runbenchmark.py randomforest openml/s/218.