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

.. only:: html

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

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

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

.. _sphx_glr_auto_tutorial_plot_woe_transformer.py:


.. _example-woe-transformer:

Converter for WOE
=================

WOE means Weights of Evidence. It consists in checking that
a feature X belongs to a series of regions - intervals -.
The results is the label of every intervals containing the feature.

.. index:: WOE, WOETransformer

A simple example
++++++++++++++++

X is a vector made of the first ten integers. Class
:class:`WOETransformer <skl2onnx.sklapi.WOETransformer>`
checks that every of them belongs to two intervals,
`]1, 3[` (leftright-opened) and `[5, 7]`
(left-right-closed). The first interval is associated
to weight 55 and and the second one to 107.

.. GENERATED FROM PYTHON SOURCE LINES 25-48

.. code-block:: default

    import os
    import numpy as np
    import pandas as pd
    from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
    from onnxruntime import InferenceSession
    import matplotlib.pyplot as plt
    from skl2onnx import to_onnx
    from skl2onnx.sklapi import WOETransformer

    # automatically registers the converter for WOETransformer
    import skl2onnx.sklapi.register  # noqa

    X = np.arange(10).astype(np.float32).reshape((-1, 1))

    intervals = [[(1.0, 3.0, False, False), (5.0, 7.0, True, True)]]
    weights = [[55, 107]]

    woe1 = WOETransformer(intervals, onehot=False, weights=weights)
    woe1.fit(X)
    prd = woe1.transform(X)
    df = pd.DataFrame({"X": X.ravel(), "woe": prd.ravel()})
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>X</th>
          <th>woe</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2.0</td>
          <td>55.0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>5</th>
          <td>5.0</td>
          <td>107.0</td>
        </tr>
        <tr>
          <th>6</th>
          <td>6.0</td>
          <td>107.0</td>
        </tr>
        <tr>
          <th>7</th>
          <td>7.0</td>
          <td>107.0</td>
        </tr>
        <tr>
          <th>8</th>
          <td>8.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>9</th>
          <td>9.0</td>
          <td>0.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-54

One Hot
+++++++

The transformer outputs one column with the weights.
But it could return one column per interval.

.. GENERATED FROM PYTHON SOURCE LINES 54-63

.. code-block:: default


    woe2 = WOETransformer(intervals, onehot=True, weights=weights)
    woe2.fit(X)
    prd = woe2.transform(X)
    df = pd.DataFrame(prd)
    df.columns = ["I1", "I2"]
    df["X"] = X
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>I1</th>
          <th>I2</th>
          <th>X</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>1.0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>55.0</td>
          <td>0.0</td>
          <td>2.0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>3.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>4.0</td>
        </tr>
        <tr>
          <th>5</th>
          <td>0.0</td>
          <td>107.0</td>
          <td>5.0</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0.0</td>
          <td>107.0</td>
          <td>6.0</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.0</td>
          <td>107.0</td>
          <td>7.0</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>8.0</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>9.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 64-66

In that case, weights can be omitted.
The output is binary.

.. GENERATED FROM PYTHON SOURCE LINES 66-75

.. code-block:: default


    woe = WOETransformer(intervals, onehot=True)
    woe.fit(X)
    prd = woe.transform(X)
    df = pd.DataFrame(prd)
    df.columns = ["I1", "I2"]
    df["X"] = X
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>I1</th>
          <th>I2</th>
          <th>X</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>1.0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1.0</td>
          <td>0.0</td>
          <td>2.0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>3.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>4.0</td>
        </tr>
        <tr>
          <th>5</th>
          <td>0.0</td>
          <td>1.0</td>
          <td>5.0</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0.0</td>
          <td>1.0</td>
          <td>6.0</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0.0</td>
          <td>1.0</td>
          <td>7.0</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>8.0</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0.0</td>
          <td>0.0</td>
          <td>9.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 76-82

Conversion to ONNX
++++++++++++++++++

*skl2onnx* implements a converter for all cases.

onehot=False

.. GENERATED FROM PYTHON SOURCE LINES 82-86

.. code-block:: default

    onx1 = to_onnx(woe1, X)
    sess = InferenceSession(onx1.SerializeToString(), providers=["CPUExecutionProvider"])
    print(sess.run(None, {"X": X})[0])





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

 .. code-block:: none

    [[  0.]
     [  0.]
     [ 55.]
     [  0.]
     [  0.]
     [107.]
     [107.]
     [107.]
     [  0.]
     [  0.]]




.. GENERATED FROM PYTHON SOURCE LINES 87-88

onehot=True

.. GENERATED FROM PYTHON SOURCE LINES 88-93

.. code-block:: default


    onx2 = to_onnx(woe2, X)
    sess = InferenceSession(onx2.SerializeToString(), providers=["CPUExecutionProvider"])
    print(sess.run(None, {"X": X})[0])





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

 .. code-block:: none

    [[  0.   0.]
     [  0.   0.]
     [ 55.   0.]
     [  0.   0.]
     [  0.   0.]
     [  0. 107.]
     [  0. 107.]
     [  0. 107.]
     [  0.   0.]
     [  0.   0.]]




.. GENERATED FROM PYTHON SOURCE LINES 94-98

ONNX Graphs
+++++++++++

onehot=False

.. GENERATED FROM PYTHON SOURCE LINES 98-116

.. code-block:: default


    pydot_graph = GetPydotGraph(
        onx1.graph,
        name=onx1.graph.name,
        rankdir="TB",
        node_producer=GetOpNodeProducer(
            "docstring", color="yellow", fillcolor="yellow", style="filled"
        ),
    )
    pydot_graph.write_dot("woe1.dot")

    os.system("dot -O -Gdpi=300 -Tpng woe1.dot")

    image = plt.imread("woe1.dot.png")
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(image)
    ax.axis("off")




.. image-sg:: /auto_tutorial/images/sphx_glr_plot_woe_transformer_001.png
   :alt: plot woe transformer
   :srcset: /auto_tutorial/images/sphx_glr_plot_woe_transformer_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (-0.5, 2674.5, 3321.5, -0.5)



.. GENERATED FROM PYTHON SOURCE LINES 117-118

onehot=True

.. GENERATED FROM PYTHON SOURCE LINES 118-136

.. code-block:: default


    pydot_graph = GetPydotGraph(
        onx2.graph,
        name=onx2.graph.name,
        rankdir="TB",
        node_producer=GetOpNodeProducer(
            "docstring", color="yellow", fillcolor="yellow", style="filled"
        ),
    )
    pydot_graph.write_dot("woe2.dot")

    os.system("dot -O -Gdpi=300 -Tpng woe2.dot")

    image = plt.imread("woe2.dot.png")
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(image)
    ax.axis("off")




.. image-sg:: /auto_tutorial/images/sphx_glr_plot_woe_transformer_002.png
   :alt: plot woe transformer
   :srcset: /auto_tutorial/images/sphx_glr_plot_woe_transformer_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (-0.5, 2743.5, 5696.5, -0.5)



.. GENERATED FROM PYTHON SOURCE LINES 137-142

Half-line
+++++++++

An interval may have only one extremity defined and the other
can be infinite.

.. GENERATED FROM PYTHON SOURCE LINES 142-152

.. code-block:: default


    intervals = [[(-np.inf, 3.0, True, True), (5.0, np.inf, True, True)]]
    weights = [[55, 107]]

    woe1 = WOETransformer(intervals, onehot=False, weights=weights)
    woe1.fit(X)
    prd = woe1.transform(X)
    df = pd.DataFrame({"X": X.ravel(), "woe": prd.ravel()})
    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>X</th>
          <th>woe</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.0</td>
          <td>55.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1.0</td>
          <td>55.0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2.0</td>
          <td>55.0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3.0</td>
          <td>55.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4.0</td>
          <td>0.0</td>
        </tr>
        <tr>
          <th>5</th>
          <td>5.0</td>
          <td>107.0</td>
        </tr>
        <tr>
          <th>6</th>
          <td>6.0</td>
          <td>107.0</td>
        </tr>
        <tr>
          <th>7</th>
          <td>7.0</td>
          <td>107.0</td>
        </tr>
        <tr>
          <th>8</th>
          <td>8.0</td>
          <td>107.0</td>
        </tr>
        <tr>
          <th>9</th>
          <td>9.0</td>
          <td>107.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 153-154

And the conversion to ONNX using the same instruction.

.. GENERATED FROM PYTHON SOURCE LINES 154-158

.. code-block:: default


    onxinf = to_onnx(woe1, X)
    sess = InferenceSession(onxinf.SerializeToString(), providers=["CPUExecutionProvider"])
    print(sess.run(None, {"X": X})[0])




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

 .. code-block:: none

    [[ 55.]
     [ 55.]
     [ 55.]
     [ 55.]
     [  0.]
     [107.]
     [107.]
     [107.]
     [107.]
     [107.]]





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

   **Total running time of the script:** (0 minutes 3.955 seconds)


.. _sphx_glr_download_auto_tutorial_plot_woe_transformer.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_woe_transformer.py <plot_woe_transformer.py>`

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

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


.. only:: html

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

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