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

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

.. _sphx_glr_examples_mlearning_transformer_plot_pipeline.py:


Pipeline
========

.. GENERATED FROM PYTHON SOURCE LINES 25-35

.. code-block:: Python


    from __future__ import annotations

    from numpy import allclose
    from numpy import linspace
    from numpy import newaxis

    from gemseo.mlearning.transformers.pipeline import Pipeline
    from gemseo.mlearning.transformers.scaler.scaler import Scaler








.. GENERATED FROM PYTHON SOURCE LINES 36-38

To illustrate the pipeline,
we consider very simple data:

.. GENERATED FROM PYTHON SOURCE LINES 38-40

.. code-block:: Python

    data = linspace(0, 1, 100)[:, newaxis]








.. GENERATED FROM PYTHON SOURCE LINES 41-44

First,
we create a pipeline of two transformers:
the first one shifts the data while the second one reduces their amplitude.

.. GENERATED FROM PYTHON SOURCE LINES 44-46

.. code-block:: Python

    pipeline = Pipeline(transformers=[Scaler(offset=1), Scaler(coefficient=2)])








.. GENERATED FROM PYTHON SOURCE LINES 47-49

Then,
we fit this :class:`.Pipeline` to the data, transform them and compute the Jacobian:

.. GENERATED FROM PYTHON SOURCE LINES 49-52

.. code-block:: Python

    transformed_data = pipeline.fit_transform(data)
    transformed_jac_data = pipeline.compute_jacobian(data)








.. GENERATED FROM PYTHON SOURCE LINES 53-55

Lastly,
we can do the same with two scalers:

.. GENERATED FROM PYTHON SOURCE LINES 55-63

.. code-block:: Python

    shifter = Scaler(offset=1)
    shifted_data = shifter.fit_transform(data)
    scaler = Scaler(coefficient=2)
    data_shifted_then_scaled = scaler.fit_transform(shifted_data)
    jac_shifted_then_scaled = scaler.compute_jacobian(
        shifted_data
    ) @ shifter.compute_jacobian(data)








.. GENERATED FROM PYTHON SOURCE LINES 64-65

and verify that the results are identical:

.. GENERATED FROM PYTHON SOURCE LINES 65-68

.. code-block:: Python

    assert allclose(transformed_data, data_shifted_then_scaled)
    assert allclose(transformed_jac_data, jac_shifted_then_scaled)








.. GENERATED FROM PYTHON SOURCE LINES 69-71

Note that a :class:`.Pipeline` can compute the Jacobian
as long as the :class:`.BaseTransformer` instances that make it up can do so.


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

   **Total running time of the script:** (0 minutes 0.003 seconds)


.. _sphx_glr_download_examples_mlearning_transformer_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>`_