.. 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. .. contents:: :local: Train a CatBoostClassifier ++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 24-52 .. code-block:: default 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) .. rst-class:: sphx-glr-script-out .. code-block:: none 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 .. raw:: html

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

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.. GENERATED FROM PYTHON SOURCE LINES 53-60 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 60-123 .. 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 124-126 Convert +++++++ .. GENERATED FROM PYTHON SOURCE LINES 126-136 .. 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 137-141 Compare the predictions +++++++++++++++++++++++ Predictions with CatBoost. .. GENERATED FROM PYTHON SOURCE LINES 141-145 .. 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 [[1] [1] [0] [1] [1]] predict_proba [[0.31557325 0.51011996 0.1743068 ]] .. GENERATED FROM PYTHON SOURCE LINES 146-147 Predictions with onnxruntime. .. GENERATED FROM PYTHON SOURCE LINES 147-154 .. code-block:: default 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]) .. rst-class:: sphx-glr-script-out .. code-block:: none predict [1 1 0 1 1] predict_proba [{0: 0.31557324528694153, 1: 0.5101199150085449, 2: 0.17430679500102997}] .. GENERATED FROM PYTHON SOURCE LINES 155-157 Final graph +++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 157-162 .. code-block:: default oinf = OnnxInference(model_onnx) ax = plot_graphviz(oinf.to_dot()) ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) .. image-sg:: /auto_tutorial/images/sphx_glr_plot_gexternal_catboost_001.png :alt: plot gexternal catboost :srcset: /auto_tutorial/images/sphx_glr_plot_gexternal_catboost_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 3.507 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 `_