.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_pipeline_lightgbm.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_examples_plot_pipeline_lightgbm.py: .. _example-lightgbm-pipe: Convert a pipeline with a LightGbm model ======================================== .. index:: LightGbm *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 *LightGbm* model. *sklearn-onnx* can convert the whole pipeline as long as it knows the converter associated to a *LGBMClassifier*. Let's see how to do it. .. contents:: :local: Train a LightGBM classifier +++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 25-59 .. code-block:: default import lightgbm import onnxmltools import skl2onnx import onnx import sklearn import matplotlib.pyplot as plt import os from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer import onnxruntime as rt from onnxruntime.capi.onnxruntime_pybind11_state import Fail as OrtFail from skl2onnx import convert_sklearn, update_registered_converter from skl2onnx.common.shape_calculator import calculate_linear_classifier_output_shapes # noqa from onnxmltools.convert.lightgbm.operator_converters.LightGbm import convert_lightgbm # noqa import onnxmltools.convert.common.data_types from skl2onnx.common.data_types import FloatTensorType import numpy from sklearn.datasets import load_iris from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from lightgbm import LGBMClassifier 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', LGBMClassifier(n_estimators=3))]) pipe.fit(X, y) .. raw:: html

Pipeline(steps=[('scaler', StandardScaler()),
                    ('lgbm', LGBMClassifier(n_estimators=3))])

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.

.. GENERATED FROM PYTHON SOURCE LINES 60-71 Register the converter for LGBMClassifier +++++++++++++++++++++++++++++++++++++++++ The converter is implemented in *onnxmltools*: `onnxmltools...LightGbm.py `_. and the shape calculator: `onnxmltools...Classifier.py `_. .. GENERATED FROM PYTHON SOURCE LINES 73-74 Then we import the converter and shape calculator. .. GENERATED FROM PYTHON SOURCE LINES 76-77 Let's register the new converter. .. GENERATED FROM PYTHON SOURCE LINES 77-82 .. code-block:: default update_registered_converter( LGBMClassifier, 'LightGbmLGBMClassifier', calculate_linear_classifier_output_shapes, convert_lightgbm, options={'nocl': [True, False], 'zipmap': [True, False, 'columns']}) .. GENERATED FROM PYTHON SOURCE LINES 83-85 Convert again +++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 85-95 .. code-block:: default model_onnx = convert_sklearn( pipe, 'pipeline_lightgbm', [('input', FloatTensorType([None, 2]))], target_opset={'': 12, 'ai.onnx.ml': 2}) # And save. with open("pipeline_lightgbm.onnx", "wb") as f: f.write(model_onnx.SerializeToString()) .. GENERATED FROM PYTHON SOURCE LINES 96-100 Compare the predictions +++++++++++++++++++++++ Predictions with LightGbm. .. GENERATED FROM PYTHON SOURCE LINES 100-104 .. 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 0 2 0 1] predict_proba [[0.29389219 0.2951622 0.41094561]] .. GENERATED FROM PYTHON SOURCE LINES 105-106 Predictions with onnxruntime. .. GENERATED FROM PYTHON SOURCE LINES 106-119 .. code-block:: default try: sess = rt.InferenceSession("pipeline_lightgbm.onnx") except OrtFail as e: print(e) print("The converter requires onnxmltools>=1.7.0") sess = None if sess is not None: 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 0 2 0 1] predict_proba [{0: 0.29389214515686035, 1: 0.29516223073005676, 2: 0.4109455943107605}] .. GENERATED FROM PYTHON SOURCE LINES 120-122 Display the ONNX graph ++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 122-137 .. code-block:: default pydot_graph = GetPydotGraph( model_onnx.graph, name=model_onnx.graph.name, rankdir="TB", node_producer=GetOpNodeProducer( "docstring", color="yellow", fillcolor="yellow", style="filled")) pydot_graph.write_dot("pipeline.dot") os.system('dot -O -Gdpi=300 -Tpng pipeline.dot') image = plt.imread("pipeline.dot.png") fig, ax = plt.subplots(figsize=(40, 20)) ax.imshow(image) ax.axis('off') .. image-sg:: /auto_examples/images/sphx_glr_plot_pipeline_lightgbm_001.png :alt: plot pipeline lightgbm :srcset: /auto_examples/images/sphx_glr_plot_pipeline_lightgbm_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (-0.5, 2030.5, 2558.5, -0.5) .. GENERATED FROM PYTHON SOURCE LINES 138-139 **Versions used for this example** .. GENERATED FROM PYTHON SOURCE LINES 139-147 .. code-block:: default print("numpy:", numpy.__version__) print("scikit-learn:", sklearn.__version__) print("onnx: ", onnx.__version__) print("onnxruntime: ", rt.__version__) print("skl2onnx: ", skl2onnx.__version__) print("onnxmltools: ", onnxmltools.__version__) print("lightgbm: ", lightgbm.__version__) .. rst-class:: sphx-glr-script-out .. code-block:: none numpy: 1.23.5 scikit-learn: 1.2.2 onnx: 1.13.1 onnxruntime: 1.14.1 skl2onnx: 1.14.0 onnxmltools: 1.11.1 lightgbm: 3.3.2 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 9.070 seconds) .. _sphx_glr_download_auto_examples_plot_pipeline_lightgbm.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_pipeline_lightgbm.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_pipeline_lightgbm.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_