Convert a pipeline with a CatBoost classifier#

sklearn-onnx only converts scikit-learn models into ONNX but many libraries implement scikit-learn API so that their models can be included in a scikit-learn pipeline. This example considers a pipeline including a :epkg:`CatBoost` model. sklearn-onnx can convert the whole pipeline as long as it knows the converter associated to a CatBoostClassifier. Let’s see how to do it.

Train a CatBoostClassifier#

from pyquickhelper.helpgen.graphviz_helper import plot_graphviz
import numpy
from onnx.helper import get_attribute_value
from sklearn.datasets import load_iris
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from mlprodict.onnxrt import OnnxInference
import onnxruntime as rt
from skl2onnx import convert_sklearn, update_registered_converter
from skl2onnx.common.shape_calculator import calculate_linear_classifier_output_shapes  # noqa
from skl2onnx.common.data_types import FloatTensorType, Int64TensorType, guess_tensor_type
from skl2onnx._parse import _apply_zipmap, _get_sklearn_operator_name
from catboost import CatBoostClassifier
from catboost.utils import convert_to_onnx_object

data = load_iris()
X =[:, :2]
y =

ind = numpy.arange(X.shape[0])
X = X[ind, :].copy()
y = y[ind].copy()

pipe = Pipeline([('scaler', StandardScaler()),
                 ('lgbm', CatBoostClassifier(n_estimators=3))]), y)
Learning rate set to 0.5
0:      learn: 0.8233591        total: 54.7ms   remaining: 109ms
1:      learn: 0.6635820        total: 55.8ms   remaining: 27.9ms
2:      learn: 0.5885989        total: 56.6ms   remaining: 0us
Pipeline(steps=[('scaler', StandardScaler()),
                 <catboost.core.CatBoostClassifier object at 0x7ff3015be440>)])
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Register the converter for CatBoostClassifier#

The model has no converter implemented in sklearn-onnx. We need to register the one coming from CatBoost itself. However, the converter does not follow sklearn-onnx design and needs to be wrapped.

def skl2onnx_parser_castboost_classifier(scope, model, inputs,
    options = scope.get_options(model, dict(zipmap=True))
    no_zipmap = isinstance(options['zipmap'], bool) and not options['zipmap']

    alias = _get_sklearn_operator_name(type(model))
    this_operator = scope.declare_local_operator(alias, model)
    this_operator.inputs = inputs

    label_variable = scope.declare_local_variable('label', Int64TensorType())
    prob_dtype = guess_tensor_type(inputs[0].type)
    probability_tensor_variable = scope.declare_local_variable('probabilities', prob_dtype)
    probability_tensor = this_operator.outputs

    if no_zipmap:
        return probability_tensor

    return _apply_zipmap(options['zipmap'], scope, model,
                         inputs[0].type, probability_tensor)

def skl2onnx_convert_catboost(scope, operator, container):
    CatBoost returns an ONNX graph with a single node.
    This function adds it to the main graph.
    onx = convert_to_onnx_object(operator.raw_operator)
    opsets = {d.domain: d.version for d in onx.opset_import}
    if '' in opsets and opsets[''] >= container.target_opset:
        raise RuntimeError(
            "CatBoost uses an opset more recent than the target one.")
    if len(onx.graph.initializer) > 0 or len(onx.graph.sparse_initializer) > 0:
        raise NotImplementedError(
            "CatBoost returns a model initializers. This option is not implemented yet.")
    if (len(onx.graph.node) not in (1, 2) or not onx.graph.node[0].op_type.startswith("TreeEnsemble") or
            (len(onx.graph.node) == 2 and onx.graph.node[1].op_type != "ZipMap")):
        types = ", ".join(map(lambda n: n.op_type, onx.graph.node))
        raise NotImplementedError(
            f"CatBoost returns {len(onx.graph.node)} != 1 (types={types}). "
            f"This option is not implemented yet.")
    node = onx.graph.node[0]
    atts = {}
    for att in node.attribute:
        atts[] = get_attribute_value(att)
        node.op_type, [operator.inputs[0].full_name],
        [operator.outputs[0].full_name, operator.outputs[1].full_name],
        op_domain=node.domain, op_version=opsets.get(node.domain, None),

    options={'nocl': [True, False], 'zipmap': [True, False, 'columns']})


model_onnx = convert_sklearn(
    pipe, 'pipeline_catboost',
    [('input', FloatTensorType([None, 2]))],
    target_opset={'': 12, '': 2})

# And save.
with open("pipeline_catboost.onnx", "wb") as f:

Compare the predictions#

Predictions with CatBoost.

print("predict", pipe.predict(X[:5]))
print("predict_proba", pipe.predict_proba(X[:1]))
predict [[2]
predict_proba [[0.09983726 0.22940648 0.67075626]]

Predictions with onnxruntime.

sess = rt.InferenceSession("pipeline_catboost.onnx")

pred_onx =, {"input": X[:5].astype(numpy.float32)})
print("predict", pred_onx[0])
print("predict_proba", pred_onx[1][:1])
predict [2 0 1 1 0]
predict_proba [{0: 0.09983726590871811, 1: 0.22940650582313538, 2: 0.6707562804222107}]

Final graph#

oinf = OnnxInference(model_onnx)
ax = plot_graphviz(oinf.to_dot())
plot gexternal catboost

Total running time of the script: ( 0 minutes 1.134 seconds)

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