Xavier Dupré

I assume this model is a pipeline using a model from torch. sklearn-onnx only converts pipeline including predictor or transformer from scikit-learn. The library gives the possibility to registrer new...

I assume you trained the pipeline with `pipeline.fit(X, y)` (it does not work for me). DictVectorizer is usually used with dictonaries where key are strings and values numbers. In that...

which version of scikit-learn are you using?

This case is not supported. It requires to concatenate strings (https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09b/sklearn/feature_extraction/_dict_vectorizer.py#L163) and this operation is not available with the standard list of onnx operators.

This operator is available in [onnxruntime-extension](https://github.com/microsoft/onnxruntime-extensions). We may use it in the converting library in a few releases.

The converser of a pipeline requires that skl2onnx knows a converter for every piece in it. The library only contains converter for scikit-learn objects. Is there a way to replace...

A parser, a converter, a shape calculator need to be implemented to support DataFrameMapper. I'd prefer to do it only if ColumnTransformer does not support what you need.

This code works with the latest version of the packages: ```python import numpy from xgboost import XGBRegressor from skl2onnx.common.shape_calculator import calculate_linear_regressor_output_shapes from skl2onnx import update_registered_converter from onnxmltools.convert.xgboost.operator_converters.XGBoost import convert_xgboost from...

I identified the bug, the conversion works before the model is dumped in json format. After being restored, the converter cannot find some information it used to find (objective). The...

It seems a bit hacky but it would work. Do you need the inverse transform on one transformer or is it a whole pipeline you need to inverse?