Impossible to convert XGBoost model into ONNX format

Question

I need to convert an XGBoost model that has been saved in the .sav format into the ONNX format. I believe it's related to the XGBoost version, but the problem is, if I update the XGBoost version to solve this problem related to ONNX, I cannot properly load the model with joblib because the portability of a .sav model is not assured for a different environment than the one the model has been trained in. So, I'm stuck! How should I handle it?

The error I got is :

Traceback (most recent call last):
  File "/mnt/c/users/dev/convert_onnx.py", line 29, in <module>
    onnx_model = convert_sklearn(model,"gbdt_model",
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/skl2onnx/convert.py", line 208, in convert_sklearn
    onnx_model = convert_topology(
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/skl2onnx/common/_topology.py", line 1532, in convert_topology
    topology.convert_operators(container=container, verbose=verbose)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/skl2onnx/common/_topology.py", line 1350, in convert_operators
    self.call_converter(operator, container, verbose=verbose)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/skl2onnx/common/_topology.py", line 1133, in call_converter
    conv(self.scopes[0], operator, container)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/skl2onnx/common/_registration.py", line 27, in __call__
    return self._fct(*args)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/onnxmltools/convert/xgboost/operator_converters/XGBoost.py", line 430, in convert_xgboost
    cls.validate(xgb_node)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/onnxmltools/convert/xgboost/operator_converters/XGBoost.py", line 283, in validate
    return XGBConverter.validate(xgb_node)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/onnxmltools/convert/xgboost/operator_converters/XGBoost.py", line 26, in validate
    params = XGBConverter.get_xgb_params(xgb_node)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/onnxmltools/convert/xgboost/operator_converters/XGBoost.py", line 22, in get_xgb_params
    return get_xgb_params(xgb_node)
  File "/mnt/c/users/dev/flask/lib/python3.10/site-packages/onnxmltools/convert/xgboost/common.py", line 40, in get_xgb_params
    gbp = config["learner"]["gradient_booster"]["gbtree_model_param"]
KeyError: 'gbtree_model_param'

The script is :

from skl2onnx import convert_sklearn
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx import get_latest_tested_opset_version
from onnxmltools.utils import save_model
import joblib
from skl2onnx import convert_sklearn, to_onnx, update_registered_converter
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx.common.shape_calculator import (
    calculate_linear_classifier_output_shapes,
    calculate_linear_regressor_output_shapes,
)
from onnxmltools.convert.xgboost.operator_converters.XGBoost import convert_xgboost
from onnxmltools.convert import convert_xgboost as convert_xgboost_booster

from xgboost import XGBClassifier
model = joblib.load("model.sav")
model = model.set_params(flatten_transform=False)
n_features = 12

target_opset = get_latest_tested_opset_version()
update_registered_converter(
    XGBClassifier,
    "XGBoostXGBClassifier",
    calculate_linear_classifier_output_shapes,
    convert_xgboost,
    options={"nocl": [True, False], "zipmap": [True, False, "columns"]},
)
onnx_model = convert_sklearn(model,"gbdt_model",
    initial_types=[("input", FloatTensorType([None, n_features]))],
    target_opset={"": target_opset, "ai.onnx.ml": 1})

save_model(onnx_model, 'model_converted.onnx')

The dependencies are:

Package              Version
-------------------- -------
blinker              1.7.0
click                8.1.7
cloudpickle          3.0.0
coloredlogs          15.0.1
contourpy            1.2.0
cycler               0.12.1
Flask                3.0.2
flatbuffers          23.5.26
fonttools            4.48.1
humanfriendly        10.0
itsdangerous         2.1.2
Jinja2               3.1.3
joblib               1.3.2
kiwisolver           1.4.5
llvmlite             0.42.0
MarkupSafe           2.1.5
matplotlib           3.8.2
mpmath               1.3.0
numba                0.59.0
numpy                1.26.4
onnx                 1.15.0
onnxconverter-common 1.14.0
onnxmltools          1.12.0
onnxruntime          1.17.0
packaging            23.2
pandas               2.2.0
pillow               10.2.0
pip                  22.0.2
protobuf             3.20.2
pydot                2.0.0
pyparsing            3.1.1
python-dateutil      2.8.2
pytz                 2024.1
PyYAML               6.0.1
scikit-learn         1.0.2
scipy                1.12.0
setuptools           59.6.0
shap                 0.44.1
six                  1.16.0
skl2onnx             1.16.0
slicer               0.0.7
sympy                1.12
threadpoolctl        3.2.0
tqdm                 4.66.2
typing_extensions    4.9.0
tzdata               2024.1
Werkzeug             3.0.1
wheel                0.37.1
xgboost              1.4.2

I have used the tutorial : https://onnx.ai/sklearn-onnx/auto_examples/plot_pipeline_xgboost.html