.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_convert_decision_function.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_convert_decision_function.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_convert_decision_function.py:


.. _l-rf-example-decision-function:

Probabilities or raw scores
===========================

A classifier usually returns a matrix of probabilities.
By default, *sklearn-onnx* creates an ONNX graph
which returns probabilities but it may skip that
step and return raw scores if the model implements
the method *decision_function*. Option ``'raw_scores'``
is used to change the default behaviour. Let's see
that on a simple example.

Train a model and convert it
++++++++++++++++++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 22-44

.. code-block:: Python


    import numpy
    import sklearn
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    import onnxruntime as rt
    import onnx
    import skl2onnx
    from skl2onnx.common.data_types import FloatTensorType
    from skl2onnx import convert_sklearn
    from sklearn.linear_model import LogisticRegression

    iris = load_iris()
    X, y = iris.data, iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y)
    clr = LogisticRegression(max_iter=500)
    clr.fit(X_train, y_train)
    print(clr)

    initial_type = [("float_input", FloatTensorType([None, 4]))]
    onx = convert_sklearn(clr, initial_types=initial_type, target_opset=12)





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

 .. code-block:: none

    LogisticRegression(max_iter=500)




.. GENERATED FROM PYTHON SOURCE LINES 45-50

Output type
+++++++++++

Let's confirm the output type of the probabilities
is a list of dictionaries with onnxruntime.

.. GENERATED FROM PYTHON SOURCE LINES 50-56

.. code-block:: Python


    sess = rt.InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])
    res = sess.run(None, {"float_input": X_test.astype(numpy.float32)})
    print("skl", clr.predict_proba(X_test[:1]))
    print("onnx", res[1][:2])





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

 .. code-block:: none

    skl [[2.18706981e-05 3.86603824e-02 9.61317747e-01]]
    onnx [{0: 2.1870704586035572e-05, 1: 0.03866042196750641, 2: 0.9613177180290222}, {0: 1.1326104868203402e-05, 1: 0.06515791267156601, 2: 0.9348307847976685}]




.. GENERATED FROM PYTHON SOURCE LINES 57-60

Raw scores and decision_function
++++++++++++++++++++++++++++++++


.. GENERATED FROM PYTHON SOURCE LINES 60-74

.. code-block:: Python


    initial_type = [("float_input", FloatTensorType([None, 4]))]
    options = {id(clr): {"raw_scores": True}}
    onx2 = convert_sklearn(
        clr, initial_types=initial_type, options=options, target_opset=12
    )

    sess2 = rt.InferenceSession(
        onx2.SerializeToString(), providers=["CPUExecutionProvider"]
    )
    res2 = sess2.run(None, {"float_input": X_test.astype(numpy.float32)})
    print("skl", clr.decision_function(X_test[:1]))
    print("onnx", res2[1][:2])





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

 .. code-block:: none

    skl [[-6.0561118   1.42131109  4.63480072]]
    onnx [{0: -6.056112289428711, 1: 1.4213112592697144, 2: 4.634799957275391}, {0: -6.659489631652832, 1: 1.997969150543213, 2: 4.661520957946777}]




.. GENERATED FROM PYTHON SOURCE LINES 75-76

**Versions used for this example**

.. GENERATED FROM PYTHON SOURCE LINES 76-82

.. code-block:: Python


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




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

 .. code-block:: none

    numpy: 2.2.0
    scikit-learn: 1.6.0
    onnx:  1.18.0
    onnxruntime:  1.21.0+cu126
    skl2onnx:  1.18.0





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

   **Total running time of the script:** (0 minutes 0.042 seconds)


.. _sphx_glr_download_auto_examples_plot_convert_decision_function.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_convert_decision_function.zip <plot_convert_decision_function.zip>`


.. only:: html

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

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