Adding your own models to the benchmark

To run your own models,

• implement an AlgInterface subclass. There are numerous examples already implemented. For models that can only run a single train-validation-test split at a time, you might want to subclass or modify SklearnSubSplitInterface from pytabkit/models/alg_interfaces/sub_split_interfaces.py. Examples can be found in pytabkit/models/alg_interfaces/other_interfaces.py or pytabkit/models/alg_interfaces/rtdl_interfaces.py.

• add an AlgInterfaceWrapper subclass. This is often just a three-liner that specifies which AlgInterfaces subclass to instantiate. See the numerous examples in pytabkit/bench/alg_wrappers/interface_wrappers.py, especially the later ones.

• adjust the code to run your AlgInterfaceWrapper on the benchmark, see scripts/run_experiments.py for many examples. Note that RunConfig has an option to save the model predictions on the whole datasets, which can significantly increase the disk usage (can be up to 2 GB per model on the meta-test-class benchmark).