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 = data.data[:, :2]
y = data.target

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

pipe = Pipeline([('scaler', StandardScaler()),
                 ('lgbm', CatBoostClassifier(n_estimators=3))])
pipe.fit(X, y)

Learning rate set to 0.5
0:      learn: 0.8352102        total: 48.7ms   remaining: 97.3ms
1:      learn: 0.6864825        total: 50.9ms   remaining: 25.4ms
2:      learn: 0.5970958        total: 52.7ms   remaining: 0us

Pipeline(steps=[('scaler', StandardScaler()),
                ('lgbm',
                 <catboost.core.CatBoostClassifier object at 0x7f20a40f0940>)])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

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,
                                         custom_parsers=None):
    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)
    this_operator.outputs.append(label_variable)
    this_operator.outputs.append(probability_tensor_variable)
    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[att.name] = get_attribute_value(att)
    container.add_node(
        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),
        **atts)


update_registered_converter(
    CatBoostClassifier,
    'CatBoostCatBoostClassifier',
    calculate_linear_classifier_output_shapes,
    skl2onnx_convert_catboost,
    parser=skl2onnx_parser_castboost_classifier,
    options={'nocl': [True, False], 'zipmap': [True, False, 'columns']})

Convert

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

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

Compare the predictions

Predictions with CatBoost.

print("predict", pipe.predict(X[:5]))
print("predict_proba", pipe.predict_proba(X[:1]))

predict [[1]
 [1]
 [0]
 [1]
 [1]]
predict_proba [[0.31557325 0.51011996 0.1743068 ]]

Predictions with onnxruntime.

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

pred_onx = sess.run(None, {"input": X[:5].astype(numpy.float32)})
print("predict", pred_onx[0])
print("predict_proba", pred_onx[1][:1])

predict [1 1 0 1 1]
predict_proba [{0: 0.31557324528694153, 1: 0.5101199150085449, 2: 0.17430679500102997}]

Final graph

oinf = OnnxInference(model_onnx)
ax = plot_graphviz(oinf.to_dot())
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)