.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/02_pre_processing/plot_01_pipeline_lvq.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_02_pre_processing_plot_01_pipeline_lvq.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_02_pre_processing_plot_01_pipeline_lvq.py:


=========
Pipelines
=========

In these examples GMLVQ is used but the same applies to all the other algorithms.
Also the `pipelines`_ feature is provided by scikit-learn and we therefore refer to scikit-learn's
documentation  for more details.

.. _pipelines: https://scikit-learn.org/stable/modules/compose.html

.. GENERATED FROM PYTHON SOURCE LINES 12-23

.. code-block:: Python


    import numpy as np
    from sklearn.datasets import load_iris
    from sklearn.metrics import classification_report
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler

    from sklvq import GMLVQ

    data, labels = load_iris(return_X_y=True)








.. GENERATED FROM PYTHON SOURCE LINES 24-29

In previous examples we used a StandardScalar instance to process the data before fitting the
model. Sklearn provides a very handy way of creating a connection between the scalar and the
model called a pipeline. The pipeline can then be used and will first call the fit method of
the standard scaler before the fit of the model. Now the data does not have to be scaled
explicitly anymore.

.. GENERATED FROM PYTHON SOURCE LINES 29-57

.. code-block:: Python


    # Create a scaler instance
    scaler = StandardScaler()

    # Create a GMLVQ model (or any other sklearn compatible estimator or other pre-processing method)
    model = GMLVQ(
        distance_type="adaptive-squared-euclidean",
        activation_type="swish",
        activation_params={"beta": 2},
        solver_type="waypoint-gradient-descent",
        solver_params={"max_runs": 10, "k": 3, "step_size": np.array([0.1, 0.05])},
        random_state=1428,
    )

    # Link them together into a single object.
    pipeline = make_pipeline(scaler, model)

    # Fit the data to the pipeline. This will first call the scaler's  fit method before passing the
    # result to  the model's fit function.
    pipeline.fit(data, labels)

    # Predict the labels using the trained pipeline. The pipeline will use the
    # mean and standard deviation it found when fit was called and applies it to the data.
    predicted_labels = pipeline.predict(data)

    # Print a classification report (sklearn)
    print(classification_report(labels, predicted_labels))





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

 .. code-block:: none

                  precision    recall  f1-score   support

               0       1.00      1.00      1.00        50
               1       0.98      0.96      0.97        50
               2       0.96      0.98      0.97        50

        accuracy                           0.98       150
       macro avg       0.98      0.98      0.98       150
    weighted avg       0.98      0.98      0.98       150





.. GENERATED FROM PYTHON SOURCE LINES 58-60

When inspecting the resulting classifier and its prototypes,
e.g., in a plot overlaid on a scatter plot of the data, don't forget to apply the scaling to the data:

.. GENERATED FROM PYTHON SOURCE LINES 60-61

.. code-block:: Python

    transformed_data = pipeline.transform(data)








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

   **Total running time of the script:** (0 minutes 0.022 seconds)


.. _sphx_glr_download_auto_examples_02_pre_processing_plot_01_pipeline_lvq.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_01_pipeline_lvq.ipynb <plot_01_pipeline_lvq.ipynb>`

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

      :download:`Download Python source code: plot_01_pipeline_lvq.py <plot_01_pipeline_lvq.py>`

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

      :download:`Download zipped: plot_01_pipeline_lvq.zip <plot_01_pipeline_lvq.zip>`


.. only:: html

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

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