.. 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. Train a LightGBM classifier +++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 22-61 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000688 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 47 [LightGBM] [Info] Number of data points in the train set: 150, number of used features: 2 [LightGBM] [Info] Start training from score -1.098612 [LightGBM] [Info] Start training from score -1.098612 [LightGBM] [Info] Start training from score -1.098612 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf .. 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 62-73 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 75-76 Then we import the converter and shape calculator. .. GENERATED FROM PYTHON SOURCE LINES 78-79 Let's register the new converter. .. GENERATED FROM PYTHON SOURCE LINES 79-87 .. code-block:: Python 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 88-90 Convert again +++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 90-102 .. code-block:: Python 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 103-107 Compare the predictions +++++++++++++++++++++++ Predictions with LightGbm. .. GENERATED FROM PYTHON SOURCE LINES 107-111 .. code-block:: Python print("predict", pipe.predict(X[:5])) print("predict_proba", pipe.predict_proba(X[:1])) .. rst-class:: sphx-glr-script-out .. code-block:: none predict [0 0 1 1 2] predict_proba [[0.51995794 0.24549283 0.23454923]] .. GENERATED FROM PYTHON SOURCE LINES 112-113 Predictions with onnxruntime. .. GENERATED FROM PYTHON SOURCE LINES 113-128 .. code-block:: Python try: sess = rt.InferenceSession( "pipeline_lightgbm.onnx", providers=["CPUExecutionProvider"] ) 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 [0 0 1 1 2] predict_proba [{0: 0.519957959651947, 1: 0.2454928159713745, 2: 0.23454922437667847}] .. GENERATED FROM PYTHON SOURCE LINES 129-131 Display the ONNX graph ++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 131-149 .. code-block:: Python 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, 2549.5, 2558.5, -0.5) .. GENERATED FROM PYTHON SOURCE LINES 150-151 **Versions used for this example** .. GENERATED FROM PYTHON SOURCE LINES 151-159 .. code-block:: Python 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: 1.26.4 scikit-learn: 1.6.dev0 onnx: 1.17.0 onnxruntime: 1.18.0+cu118 skl2onnx: 1.17.0 onnxmltools: 1.13.0 lightgbm: 4.2.0 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.752 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-jupyter :download:`Download Jupyter notebook: plot_pipeline_lightgbm.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_pipeline_lightgbm.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_