Comments (4)
@RAMitchell Indeed, we should plan to add back-end optimizations. Right now, treelite simply converts a tree model into hierarchical if-else statements. Tree transformations will be an essential element.
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@RAMitchell Sorry for the delay. I've been preoccupied with making last-minute fixes before the official launch.
I already see that the compiler portion needs some re-working; right now, it's a giant blob of script that generates C program on the fly. Really, there needs to be a good intermediate representation to enable the kind of work you and I would like to do (transformation).
Can we schedule a time for a phone or Skype call to discuss this topic further?
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@RAMitchell I have just started looking into model compression / pruning for decision trees. One thing we'd need to decide is a set of transformations we should support. Right now, I'm trying to reach out to potential users and customers and learn the concrete use cases of model compression for trees.
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Closing this for now. Right now, Treelite model object is used as an Intermediate Representation for cuML's Forest Inference Library.
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Related Issues (20)
- Clean up serialization logic
- Support XGBoost gblinear Booster HOT 1
- Release version 3.3.0
- Release version 3.4.0
- Replace setup.py with pyproject.toml
- Treelite crashes with XGBoost 2.0 dev
- Document Treelite serialization format.
- Adopt Four-Document System to organize docs
- Refactor sklearn loader using mix-in classes
- Implement v4 serialization format
- Revamp JSON importer to make it easy to use
- Drop "max_index" postprocessor
- Add directory exist check in _load_lib for add_dll_directory HOT 1
- scikit-learn 1.4.0 breaks unit test HOT 1
- Multi-class, multi-output RandomForestClassifier in scikit-learn produces error
- Test multi-target models where each tree predicts multiple regression targets HOT 1
- Export model back to scikit-learn HOT 1
- NumPy 2.0 support HOT 4
- xgboost json model fails to load HOT 3
- Release 4.2.0
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