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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_examples_mlearning_transformer_plot_scaler.py>`
        to download the full example code

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

.. _sphx_glr_examples_mlearning_transformer_plot_scaler.py:


Scaler example
==============

In this example, we will create a scaler to transform data.

.. GENERATED FROM PYTHON SOURCE LINES 29-50

.. code-block:: default

    from __future__ import absolute_import, division, print_function, unicode_literals

    from builtins import zip

    import matplotlib.pyplot as plt
    from future import standard_library
    from numpy import linspace
    from numpy import max as npmax
    from numpy import mean
    from numpy import min as npmin
    from numpy import sin, std

    from gemseo.api import configure_logger
    from gemseo.mlearning.transform.scaler.min_max_scaler import MinMaxScaler
    from gemseo.mlearning.transform.scaler.scaler import Scaler
    from gemseo.mlearning.transform.scaler.standard_scaler import StandardScaler

    configure_logger()

    standard_library.install_aliases()








.. GENERATED FROM PYTHON SOURCE LINES 51-53

Create dataset
--------------

.. GENERATED FROM PYTHON SOURCE LINES 53-57

.. code-block:: default

    x = linspace(0, 1, 100)
    data = (x < 0.3) * 5 * x + (x > 0.3) * sin(20 * x)









.. GENERATED FROM PYTHON SOURCE LINES 58-60

Create transformers
---------------------------

.. GENERATED FROM PYTHON SOURCE LINES 60-65

.. code-block:: default

    same_scaler = Scaler()
    scaler = Scaler(offset=-2, coefficient=0.5)
    min_max_scaler = MinMaxScaler()
    standard_scaler = StandardScaler()








.. GENERATED FROM PYTHON SOURCE LINES 66-68

Transform data
--------------

.. GENERATED FROM PYTHON SOURCE LINES 68-73

.. code-block:: default

    same_data = same_scaler.fit_transform(data)
    scaled_data = scaler.fit_transform(data)
    min_max_scaled_data = min_max_scaler.fit_transform(data)
    standard_scaled_data = standard_scaler.fit_transform(data)








.. GENERATED FROM PYTHON SOURCE LINES 74-76

Compute jacobian
----------------

.. GENERATED FROM PYTHON SOURCE LINES 76-83

.. code-block:: default

    jac_same = same_scaler.compute_jacobian(data)
    jac_scaled = scaler.compute_jacobian(data)
    jac_min_max_scaled = min_max_scaler.compute_jacobian(data)
    jac_standard_scaled = standard_scaler.compute_jacobian(data)

    print(jac_standard_scaled)





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

 Out:

 .. code-block:: none

    [[1.42827181 0.         0.         ... 0.         0.         0.        ]
     [0.         1.42827181 0.         ... 0.         0.         0.        ]
     [0.         0.         1.42827181 ... 0.         0.         0.        ]
     ...
     [0.         0.         0.         ... 1.42827181 0.         0.        ]
     [0.         0.         0.         ... 0.         1.42827181 0.        ]
     [0.         0.         0.         ... 0.         0.         1.42827181]]




.. GENERATED FROM PYTHON SOURCE LINES 84-92

Print properties
----------------
We may print the min, max, mean and standard deviation of the transformed
data. This reveals some of the properties of the different scalers: The
scaler without arguments has an offset of 0 and a scaling coefficient of 1,
which turns this transformer into the identity function. The min-max scaler
has a min of 0 and a max of 1. The standard scaler has a mean of zero and
a standard deviation of 1.

.. GENERATED FROM PYTHON SOURCE LINES 92-109

.. code-block:: default

    names = [
        "Original data  ",
        "Same scaler    ",
        "Scaler(-2, 0.5)",
        "Min-max scaler ",
        "Standard scaler",
    ]
    print("{:^18}{:^8}{:^8}{:^8}{:^8}".format("", "min", "max", "mean", "std"))
    for name, y in zip(
        names, [data, same_data, scaled_data, min_max_scaled_data, standard_scaled_data]
    ):
        print(
            "{} : {: .3f}, {: .3f}, {: .3f}, {: .3f}".format(
                name, npmin(y), npmax(y), mean(y), std(y)
            ),
        )





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

 Out:

 .. code-block:: none

                        min     max     mean    std   
    Original data   : -0.996,  1.465,  0.251,  0.700
    Same scaler     : -0.996,  1.465,  0.251,  0.700
    Scaler(-2, 0.5) : -2.498, -1.268, -1.874,  0.350
    Min-max scaler  :  0.000,  1.000,  0.507,  0.285
    Standard scaler : -1.782,  1.733,  0.000,  1.000




.. GENERATED FROM PYTHON SOURCE LINES 110-112

Plot data
---------

.. GENERATED FROM PYTHON SOURCE LINES 112-119

.. code-block:: default

    plt.plot(x, data, label="Original")
    plt.plot(x, same_data, label="Identity scaled", linestyle="--")
    plt.plot(x, scaled_data, label="Scaled(-2, 0.5)")
    plt.plot(x, min_max_scaled_data, label="Min-max")
    plt.plot(x, standard_scaled_data, label="Standard")
    plt.legend()
    plt.show()



.. image:: /examples/mlearning/transformer/images/sphx_glr_plot_scaler_001.png
    :alt: plot scaler
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.114 seconds)


.. _sphx_glr_download_examples_mlearning_transformer_plot_scaler.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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