.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_tutorial/plot_gexternal_catboost.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_tutorial_plot_gexternal_catboost.py: .. _example-catboost: Convert a pipeline with a CatBoost classifier ============================================= .. index:: CatBoost :epkg:`sklearn-onnx` only converts :epkg:`scikit-learn` models into *ONNX* but many libraries implement :epkg:`scikit-learn` API so that their models can be included in a :epkg:`scikit-learn` pipeline. This example considers a pipeline including a :epkg:`CatBoost` model. :epkg:`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 ++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 21-54 .. code-block:: default 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 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) .. rst-class:: sphx-glr-script-out .. code-block:: none Learning rate set to 0.5 0: learn: 0.8212475 total: 58.2ms remaining: 116ms 1: learn: 0.6738254 total: 59.9ms remaining: 30ms 2: learn: 0.5837067 total: 60.5ms remaining: 0us .. raw:: html

Pipeline(steps=[('scaler', StandardScaler()),
                    ('lgbm',
                     <catboost.core.CatBoostClassifier object at 0x7f5eb565f850>)])

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.. GENERATED FROM PYTHON SOURCE LINES 55-62 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. .. GENERATED FROM PYTHON SOURCE LINES 62-135 .. code-block:: default 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"]}, ) .. GENERATED FROM PYTHON SOURCE LINES 136-138 Convert +++++++ .. GENERATED FROM PYTHON SOURCE LINES 138-150 .. code-block:: default 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()) .. GENERATED FROM PYTHON SOURCE LINES 151-155 Compare the predictions +++++++++++++++++++++++ Predictions with CatBoost. .. GENERATED FROM PYTHON SOURCE LINES 155-159 .. code-block:: default print("predict", pipe.predict(X[:5])) print("predict_proba", pipe.predict_proba(X[:1])) .. rst-class:: sphx-glr-script-out .. code-block:: none predict [[2] [1] [2] [2] [2]] predict_proba [[0.15038602 0.38990275 0.45971123]] .. GENERATED FROM PYTHON SOURCE LINES 160-161 Predictions with onnxruntime. .. GENERATED FROM PYTHON SOURCE LINES 161-167 .. code-block:: default sess = rt.InferenceSession("pipeline_catboost.onnx", providers=["CPUExecutionProvider"]) pred_onx = sess.run(None, {"input": X[:5].astype(numpy.float32)}) print("predict", pred_onx[0]) print("predict_proba", pred_onx[1][:1]) .. rst-class:: sphx-glr-script-out .. code-block:: none predict [2 1 2 2 2] predict_proba [{0: 0.1503860205411911, 1: 0.3899027407169342, 2: 0.4597112238407135}] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.425 seconds) .. _sphx_glr_download_auto_tutorial_plot_gexternal_catboost.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gexternal_catboost.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gexternal_catboost.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_