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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_glr_examples_view_plot_pca.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_glr_examples_view_plot_pca.py:


=================================================
Plot robust principal components analysis (PCA)
=================================================

visualizes the robust PCA component analysis from hydro-geological data

.. GENERATED FROM PYTHON SOURCE LINES 8-11

.. code-block:: Python

    # Author: L.Kouadio 
    # Licence: BSD-3-clause 








.. GENERATED FROM PYTHON SOURCE LINES 12-13

Visualize the first two components PC1 and PC2

.. GENERATED FROM PYTHON SOURCE LINES 13-30

.. code-block:: Python


    from watex.datasets import load_bagoue 
    from watex.view.mlplot import EvalPlot 
    X , y = load_bagoue(as_frame =True )
    b=EvalPlot(tname ='flow', encode_labels=True ,
                              scale = True )
    b.fit_transform (X, y)
    b.plotPCA (n_components= 2 )
    # Note that pc1 and pc2 labels > n_components -> otherwise  raises user warnings
    # Axis 1 and 2 is the default behaviour. 
    # Runing the script below shows the same figure as the above. 

    # b.plotPCA (n_components= 2 , biplot=False, pc1_label='Axis 1',
    #                       pc2_label='axis 2')
    # UserWarning: Number of components and axes might be consistent;
    # '2'and '4 are given; default two components are used.
   



.. image-sg:: /glr_examples/view/images/sphx_glr_plot_pca_001.png
   :alt: PCA
   :srcset: /glr_examples/view/images/sphx_glr_plot_pca_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    EvalPlot(tname= flow, objective= None, scale= True, ... , sns_height= 4.0, sns_aspect= 0.7, verbose= 0)



.. GENERATED FROM PYTHON SOURCE LINES 31-34

can visulizalise the other components axis in Axis 3 and 4. Note for PC1 and PC2 labels must be consistent
with the number of components. 


.. GENERATED FROM PYTHON SOURCE LINES 34-37

.. code-block:: Python

    b.plotPCA (n_components= 8 , biplot=False, pc1_label='Axis3',
                             pc2_label='axis4')
    # # works fine since n_components are greater to the number of axes



.. image-sg:: /glr_examples/view/images/sphx_glr_plot_pca_002.png
   :alt: PCA
   :srcset: /glr_examples/view/images/sphx_glr_plot_pca_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    EvalPlot(tname= flow, objective= None, scale= True, ... , sns_height= 4.0, sns_aspect= 0.7, verbose= 0)




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

   **Total running time of the script:** (0 minutes 0.829 seconds)


.. _sphx_glr_download_glr_examples_view_plot_pca.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/watex/watex/0.3.X?urlpath=lab/tree/notebooks/glr_examples/view/plot_pca.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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