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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_pipeline.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_pipeline.py:


Draw a pipeline
===============

There is no other way to look into one model stored
in ONNX format than looking into its node with
*onnx*. This example demonstrates
how to draw a model and to retrieve it in *json*
format.

Retrieve a model in JSON format
+++++++++++++++++++++++++++++++

That's the most simple way.

.. GENERATED FROM PYTHON SOURCE LINES 19-47

.. code-block:: Python


    import skl2onnx
    import onnxruntime
    import sklearn
    import numpy
    import matplotlib.pyplot as plt
    import os
    from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
    from onnx import ModelProto
    import onnx
    from skl2onnx.algebra.onnx_ops import OnnxAdd, OnnxMul

    onnx_fct = OnnxAdd(
        OnnxMul("X", numpy.array([2], dtype=numpy.float32), op_version=12),
        numpy.array([[1, 0], [0, 1]], dtype=numpy.float32),
        output_names=["Y"],
        op_version=12,
    )

    X = numpy.array([[4, 5], [-2, 3]], dtype=numpy.float32)
    model = onnx_fct.to_onnx({"X": X}, target_opset=12)
    print(model)

    filename = "example1.onnx"
    with open(filename, "wb") as f:
        f.write(model.SerializeToString())






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

 .. code-block:: none

    ir_version: 7
    opset_import {
      domain: ""
      version: 12
    }
    producer_name: "skl2onnx"
    producer_version: "1.17.0"
    domain: "ai.onnx"
    model_version: 0
    graph {
      node {
        input: "X"
        input: "Mu_Mulcst"
        output: "Mu_C0"
        name: "Mu_Mul"
        op_type: "Mul"
        domain: ""
      }
      node {
        input: "Mu_C0"
        input: "Ad_Addcst"
        output: "Y"
        name: "Ad_Add"
        op_type: "Add"
        domain: ""
      }
      name: "OnnxAdd"
      initializer {
        dims: 1
        data_type: 1
        float_data: 2
        name: "Mu_Mulcst"
      }
      initializer {
        dims: 2
        dims: 2
        data_type: 1
        float_data: 1
        float_data: 0
        float_data: 0
        float_data: 1
        name: "Ad_Addcst"
      }
      input {
        name: "X"
        type {
          tensor_type {
            elem_type: 1
            shape {
              dim {
              }
              dim {
                dim_value: 2
              }
            }
          }
        }
      }
      output {
        name: "Y"
        type {
          tensor_type {
            elem_type: 1
            shape {
              dim {
                dim_value: 2
              }
              dim {
                dim_value: 2
              }
            }
          }
        }
      }
    }





.. GENERATED FROM PYTHON SOURCE LINES 48-55

Draw a model with ONNX
++++++++++++++++++++++
We use `net_drawer.py
<https://github.com/onnx/onnx/blob/main/onnx/tools/net_drawer.py>`_
included in *onnx* package.
We use *onnx* to load the model
in a different way than before.

.. GENERATED FROM PYTHON SOURCE LINES 55-62

.. code-block:: Python



    model = ModelProto()
    with open(filename, "rb") as fid:
        content = fid.read()
        model.ParseFromString(content)








.. GENERATED FROM PYTHON SOURCE LINES 63-64

We convert it into a graph.

.. GENERATED FROM PYTHON SOURCE LINES 64-72

.. code-block:: Python

    pydot_graph = GetPydotGraph(
        model.graph,
        name=model.graph.name,
        rankdir="TB",
        node_producer=GetOpNodeProducer("docstring"),
    )
    pydot_graph.write_dot("graph.dot")








.. GENERATED FROM PYTHON SOURCE LINES 73-74

Then into an image

.. GENERATED FROM PYTHON SOURCE LINES 74-76

.. code-block:: Python

    os.system("dot -O -Tpng graph.dot")





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

 .. code-block:: none


    0



.. GENERATED FROM PYTHON SOURCE LINES 77-78

Which we display...

.. GENERATED FROM PYTHON SOURCE LINES 78-82

.. code-block:: Python

    image = plt.imread("graph.dot.png")
    plt.imshow(image)
    plt.axis("off")




.. image-sg:: /auto_examples/images/sphx_glr_plot_pipeline_001.png
   :alt: plot pipeline
   :srcset: /auto_examples/images/sphx_glr_plot_pipeline_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (-0.5, 431.5, 602.5, -0.5)



.. GENERATED FROM PYTHON SOURCE LINES 83-84

**Versions used for this example**

.. GENERATED FROM PYTHON SOURCE LINES 84-90

.. code-block:: Python


    print("numpy:", numpy.__version__)
    print("scikit-learn:", sklearn.__version__)
    print("onnx: ", onnx.__version__)
    print("onnxruntime: ", onnxruntime.__version__)
    print("skl2onnx: ", skl2onnx.__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





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

   **Total running time of the script:** (0 minutes 0.652 seconds)


.. _sphx_glr_download_auto_examples_plot_pipeline.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.ipynb <plot_pipeline.ipynb>`

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

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


.. only:: html

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

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