Add missing dependency onnxconverter_common, fix multi regression with xgboost

[xadupre]

Fixes issue #673, #676

[casassg]

@xadupre any chance to get a release for this version?

[devspatron]

Hello Xavier Dupré, its blurshift again with the same problem i have ran this code on google colab

---

the code

code= "
//export models as ONNX
!pip install onnx
!pip install onnxmltools
!pip install skl2onnx
!pip install keras2onnx
!pip install onnxruntime

//!pip uninstall protobuf
//!pip install protobuf==4.21.9
!pip install protobuf==3.20.*
!pip freeze | grep protobuf

//use my cpu specifi version
!pip install -U xgboost==1.7.5

//code as follows:
import xgboost
import numpy as np
import onnxmltools
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx import update_registered_converter

//Your data and labels [-Ox (253, 84) -Oy (253, 112) -]
X = np.random.rand(100, 10 )
y = np.random.rand(100, 210 )

//Train XGBoost regressor
model = xgboost.XGBRegressor(objective='reg:squarederror', n_estimators=10, );
model.fit(X, y);

//Define input type (adjust shape according to your input)
num_features0 = X.shape[-1];
initial_type = [('float_input', FloatTensorType([None, num_features0 ]))]

//Convert XGBoost model to ONNX
onnx_model = convert_sklearn( model, initial_types=initial_type, target_opset=12, );
//onnx_model = onnxmltools.convert_xgboost( model=model, initial_types=[('input', FloatTensorType([None, num_features0 ]))], );

//Save the ONNX model /content/sample_data
with open( "/content/xgboost_model.onnx", "wb" ) as f:
f.write( onnx_model.SerializeToString())

";

---

the error

the error i get
error ="

MissingShapeCalculator Traceback (most recent call last)

in <cell line: 22>()
20
21 # Convert XGBoost model to ONNX
---> 22 onnx_model = convert_sklearn( model, initial_types=initial_type, target_opset=12, );
23 # onnx_model = onnxmltools.convert_xgboost( model=model, initial_types=[('input', FloatTensorType([None, num_features0 ]))], );
24

4 frames

/usr/local/lib/python3.10/dist-packages/skl2onnx/common/_topology.py in infer_types(self)
628 # Invoke a core inference function
629 if self.type is None:
--> 630 raise MissingShapeCalculator(
631 "Unable to find a shape calculator for type '{}'.".format(
632 type(self.raw_operator)

MissingShapeCalculator: Unable to find a shape calculator for type '<class 'xgboost.sklearn.XGBRegressor'>'.
It usually means the pipeline being converted contains a
transformer or a predictor with no corresponding converter
implemented in sklearn-onnx. If the converted is implemented
in another library, you need to register
the converted so that it can be used by sklearn-onnx (function
update_registered_converter). If the model is not yet covered
by sklearn-onnx, you may raise an issue to
https://github.com/onnx/sklearn-onnx/issues
to get the converter implemented or even contribute to the
project. If the model is a custom model, a new converter must
be implemented. Examples can be found in the gallery.

";

This problem still persists, even after installing new packages, Could you try running my code on google colab and try to examine the resulting converted onnx model using 'https://netron.app/'. Ensure the app has output shape=(-1, 210).
Thank you.