.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_gexternal_lightgbm.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_gexternal_lightgbm.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_gexternal_lightgbm.py:


.. _example-lightgbm:

Convert a pipeline with a LightGBM model
========================================

.. index:: LightGBM

: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:`LightGBM` model. :epkg:`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 22-48

.. code-block:: default

    from pyquickhelper.helpgen.graphviz_helper import plot_graphviz
    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 onnxmltools.convert.lightgbm.operator_converters.LightGbm import convert_lightgbm  # noqa
    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

 Out:

 .. code-block:: none


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



.. GENERATED FROM PYTHON SOURCE LINES 49-60

Register the converter for LGBMClassifier
+++++++++++++++++++++++++++++++++++++++++

The converter is implemented in :epkg:`onnxmltools`:
`onnxmltools...LightGbm.py
<https://github.com/onnx/onnxmltools/blob/master/onnxmltools/convert/
lightgbm/operator_converters/LightGbm.py>`_.
and the shape calculator:
`onnxmltools...Classifier.py
<https://github.com/onnx/onnxmltools/blob/master/onnxmltools/convert/
lightgbm/shape_calculators/Classifier.py>`_.

.. GENERATED FROM PYTHON SOURCE LINES 60-66

.. 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 67-69

Convert again
+++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 69-79

.. code-block:: default


    model_onnx = convert_sklearn(
        pipe, 'pipeline_lightgbm',
        [('input', FloatTensorType([None, 2]))],
        target_opset=12, options={'lgbm__zipmap': False})

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








.. GENERATED FROM PYTHON SOURCE LINES 80-84

Compare the predictions
+++++++++++++++++++++++

Predictions with LightGbm.

.. GENERATED FROM PYTHON SOURCE LINES 84-88

.. code-block:: default


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





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    predict [1 1 2 0 1]
    predict_proba [[0.28897427 0.42250751 0.28851822]]




.. GENERATED FROM PYTHON SOURCE LINES 89-90

Predictions with onnxruntime.

.. GENERATED FROM PYTHON SOURCE LINES 90-97

.. code-block:: default


    sess = rt.InferenceSession("pipeline_lightgbm.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

 Out:

 .. code-block:: none

    predict [1 1 2 0 1]
    predict_proba [[0.2889743  0.42250752 0.28851822]]




.. GENERATED FROM PYTHON SOURCE LINES 98-100

Final graph
+++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 100-106

.. 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:: /auto_examples/images/sphx_glr_plot_gexternal_lightgbm_001.png
    :alt: plot gexternal lightgbm
    :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  4.208 seconds)


.. _sphx_glr_download_auto_examples_plot_gexternal_lightgbm.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/sdpython/onnxcustom/master?urlpath=lab/tree/notebooks/auto_examples/plot_gexternal_lightgbm.ipynb
      :alt: Launch binder
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_gexternal_lightgbm.py <plot_gexternal_lightgbm.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_gexternal_lightgbm.ipynb <plot_gexternal_lightgbm.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_