Xavier Dupré

The scope of sklearn-onnx is scikit-learn. Adding it to kmodes means writing the converter and submit a PR to the owner of the library.

Did you train it with boolean labels?

Is it possible to cast the booleans into integers? That would the simplest way here. In a short term, I could include that in the error message.

That does not seem too complicated. It is urgent?

I exposed methods predict, decision_function. Option score_samples can be True to add a third result for method score_samples. I'll look into negative_outlier_factor_ once everything else is fixed. It would be...

The first PR was merge. The converter is now available. About negative_outlier_factor_, it is a constant and I prefer not to add an option to expose it as a result....

The converter released on PYPI. Closing the issue. Feel free to reopen.

Sorry the for the delay, the training was initially out of scope. One way to solve your issue is to add a function to replace a constant in the ONNX...

The training is specific to LOF, I did not have time to think about a generic design but both can be handled separately.

Reverr onnx model is easier with tensorflow because you can express most of the onnx operator with tensorfoow functions. With scikit-learn, we could translate the onnx graph into a python...