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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_principal_components.py:


============================
Principal Component Analysis
============================

Principal Component Analysis is a feature reduction (decomposition) technique that aims to maximise the 
retained variance in less features.  It is a tool to help manage the "curse of dimensionality".

.. GENERATED FROM PYTHON SOURCE LINES 10-23

.. code-block:: default

    import logging

    import pandas as pd
    import plotly.io as pio
    import plotly.express as px
    from sklearn.datasets import load_diabetes

    from elphick.sklearn_viz.features import plot_principal_components, plot_scatter_matrix, \
        plot_explained_variance, PrincipalComponents

    logging.basicConfig(level=logging.DEBUG,
                        format='%(asctime)s %(levelname)s %(module)s - %(funcName)s: %(message)s',
                        datefmt='%Y-%m-%dT%H:%M:%S%z')







.. GENERATED FROM PYTHON SOURCE LINES 24-26

Load Classification Data
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 26-32

.. code-block:: default


    df = px.data.iris().drop(columns=['species_id'])
    df['species'] = df['species'].astype('category')
    x = df[[col for col in df.columns if col != 'species']]
    y = df['species']








.. GENERATED FROM PYTHON SOURCE LINES 33-35

Plot Classification Data
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 37-39

SPLOM - Original Feature Space
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 39-44

.. code-block:: default


    fig = plot_scatter_matrix(x=x, y=y, original_features=True)
    fig.update_layout(height=800)
    fig






.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 45-47

SPLOM - Principal Components
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 47-52

.. code-block:: default


    fig = plot_scatter_matrix(x=x, y=y)
    fig.update_layout(height=800)
    fig






.. raw:: html

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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 53-55

Scatter - 2D PCA
^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 55-60

.. code-block:: default


    fig = plot_principal_components(x=x, color=y, plot_3d=False, loading_vectors=False)
    fig.update_layout(height=800)
    fig






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.. GENERATED FROM PYTHON SOURCE LINES 61-62

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.. GENERATED FROM PYTHON SOURCE LINES 62-67

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.. GENERATED FROM PYTHON SOURCE LINES 68-70

Explained Variance
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.. GENERATED FROM PYTHON SOURCE LINES 70-74

.. code-block:: default


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.. GENERATED FROM PYTHON SOURCE LINES 75-77

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.. GENERATED FROM PYTHON SOURCE LINES 77-82

.. code-block:: default


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.. GENERATED FROM PYTHON SOURCE LINES 83-84

Plotting loading vectors is the default.

.. GENERATED FROM PYTHON SOURCE LINES 84-88

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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 89-94

Regression Datasets
-------------------

The preceding examples demonstrated a categorical target variable.
Regression problems with a numeric variable are also supported.

.. GENERATED FROM PYTHON SOURCE LINES 94-100

.. code-block:: default


    diabetes = load_diabetes(as_frame=True, scaled=False)
    x, y = diabetes.data, diabetes.target.rename('target')
    df = pd.concat([x, y], axis=1)
    df.shape





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

 .. code-block:: none


    (442, 11)



.. GENERATED FROM PYTHON SOURCE LINES 101-105

.. code-block:: default

    fig = plot_principal_components(x=x, color=y, plot_3d=False)
    fig.update_layout(height=800)
    fig






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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 106-108

This dataset requires more variables to retain a reasonable proportion of the total variance compared
to the iris dataset as indicated in the section below.

.. GENERATED FROM PYTHON SOURCE LINES 110-114

Accessing the Data
------------------

By plotting with the object rather than the function you can access the data.

.. GENERATED FROM PYTHON SOURCE LINES 114-119

.. code-block:: default


    pca = PrincipalComponents(x=x, color=y)
    fig = pca.plot_explained_variance()
    fig






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.. GENERATED FROM PYTHON SOURCE LINES 120-120

.. code-block:: default

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

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    {'raw': PCResults(data=           PC1        PC2        PC3  ...       PC8       PC9      PC10
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    4     8.052537  -2.108751  -0.220784  ... -0.353808  0.429188 -0.034163
    ..         ...        ...        ...  ...       ...       ...       ...
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    441  52.914252 -53.019841  32.987254  ...  0.928400  0.663407  0.243303

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    Name: explained_variance, dtype: float64, loadings=           PC1        PC2       PC3  ...       PC8       PC9      PC10
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   **Total running time of the script:** ( 0 minutes  0.719 seconds)


.. _sphx_glr_download_auto_examples_principal_components.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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