Extends scikit-learn with a couple of new models, transformers, metrics, plotting.
Project description
onnxcustom: custom ONNX
Tutorial on how to convert machine learned models into ONNX, implement your own converter or runtime. The module must be compiled to be used inplace:
python setup.py build_ext --inplace
Generate the setup in subfolder dist:
python setup.py sdist
Generate the documentation in folder dist/html:
python setup.py build_sphinx
Run the unit tests:
python setup.py unittests
To check style:
python -m flake8 onnxcustom tests examples
The function check or the command line python -m onnxcustom check checks the module is properly installed and returns processing time for a couple of functions or simply:
import onnxcustom onnxcustom.check()
This tutorial has been merged into sklearn-onnx documentation.
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