Comments (9)
@rspadim It is the job of Treelite contributors to support Catboost, not Catboost contributors. I think you should close catboost/catboost#721.
Also re-opening this issue, as a reminder that we should support Catboost
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@rspadim Are you familiar with how Catboot saves models?
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there're some types, and very interesting since some are wellknown formats (standards)
i will point it here, and try to upload some samples
https://github.com/catboost/catboost/blob/master/catboost/libs/model/model.cpp#L174
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well... now i know that it can export to python and c++ too
sorry my fault
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@rspadim It may be still beneficial to add Catboost support in Treelite, if you'd like to generate pure C code without external dependency. That said, closing for now.
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an exampel to create models:
from sklearn.datasets import load_boston
X, y = load_boston(return_X_y=True)
print('dimensions of X = {}'.format(X.shape))
print('dimensions of y = {}'.format(y.shape))
from catboost import CatBoost
model = CatBoost()
model.fit(X, y)
for i in ['cpp', 'python', 'json', 'cbm', 'onnx', 'coreml']:
print(i)
model.save_model(fname="teste.catboost." + i, format=i)
print("hello")
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hi @hcho3 , yes i agree with you, pure C is the best think treelite have, it's very intuitive to port to any language
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Nice, reference to create treelite support
cpp might be easy to port
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Closing in favor of #377
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Related Issues (20)
- Add first-class support for multi-output models in Treelite 4.0
- API refactors
- Documentation for library writers HOT 1
- treelite prediction 4x slower than xgboost HOT 3
- Document that Left child is chosen when condition is evaluated True
- Use std::variant to implement type-based dispatching
- Do not call np.squeeze on output of predict_leaf() / predict_per_tree() HOT 1
- treelite::ConcatenateModelObjects() ought to set threshold_type and leaf_output_type fields
- 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
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