Fix Rotbaum serialization and deserialization #3068
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lostella
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Description of changes:
Prior to these changes, serializing a TreePredictor would only save the constructor arguments for the class. A trained TreePredictor creates a model_list within the predictor object that includes n trained tree models for n prediction length. This change pickles that model_list during serialization and reads it during deserialization. This change will enable Rotbaum to perform predictions after serialization and deserialization.
This request also includes a guardrail to prevent prediction failures after serialization and deserialization if dataset doesn't contain feat_static_cat and cardinality was initially set to
auto. In scenarios ofautocardinality, PreprocessOnlyLagFeatures class infers the cardinality during training and sets it within the preprocess object in TreePredictor class. Pickling this object during serialization tends to be very memory intensive (100MB+). To avoid pickling this object, this guardrail also checks whether the dataset has feat_static_cat instead of just relying on the value of cardinality.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.
Please tag this pr with at least one of these labels to make our release process faster: BREAKING, new feature, bug fix, other change, dev setup