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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_compare_datasets.py:


Compare Multiple N-D datasets
=============================

Comparing 1D datasets readily achieved by overlaying distributions, boxplots, etc.
For multivariate (N-D) datasets, things get a little more difficult.

This example applies Principal Component Analysis by group variable and colors the loading vectors by group.

.. GENERATED FROM PYTHON SOURCE LINES 11-23

.. code-block:: default

    import logging

    import pandas as pd
    import plotly.io

    from elphick.sklearn_viz.features import plot_parallel_coordinates
    from elphick.sklearn_viz.features.principal_components import plot_loading_vectors, plot_correlation_circle, \
        plot_explained_variance, plot_principal_components

    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

Create a dataset
----------------

.. GENERATED FROM PYTHON SOURCE LINES 26-59

.. code-block:: default



    import numpy as np

    # for consistent results
    np.random.seed(7)

    n_samples = 125
    n_outliers = 25
    n_features = 4

    # generate Gaussian data of shape (125, 4)
    cov1 = np.array([[9, -7, -2, -2],
                     [-7, 7, 1.5, 1],
                     [-2, 1.5, 1, 0.5],
                     [-2, 1, 0.5, 0.5]])
    cov2 = np.array([[5, -2, -1.5, -3],
                     [-2, 2, 0.5, 0.5],
                     [-1.5, 0.5, 1, 1],
                     [-3, 0.5, 1, 3]])

    x1 = np.dot(np.random.randn(n_samples, n_features), cov1)
    x2 = np.dot(np.random.randn(n_samples, n_features), cov2)

    df_x1: pd.DataFrame = pd.DataFrame(x1, columns=[f"F{n}" for n in range(1, n_features + 1)])
    # shift the mean on two features
    df_x1['F4'] = df_x1['F4'] - 2.0
    df_x1['F2'] = df_x1['F2'] + 1.0
    df_x2: pd.DataFrame = pd.DataFrame(x2, columns=[f"F{n}" for n in range(1, n_features + 1)])
    x = pd.concat([df_x1.assign(group='one'), df_x2.assign(group='two')], axis=0).reset_index(drop=True)
    x['group'] = pd.Categorical(x['group'])
    x






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>F1</th>
          <th>F2</th>
          <th>F3</th>
          <th>F4</th>
          <th>group</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>17.595620</td>
          <td>-13.638495</td>
          <td>-3.843379</td>
          <td>-5.626821</td>
          <td>one</td>
        </tr>
        <tr>
          <th>1</th>
          <td>-3.603537</td>
          <td>4.780860</td>
          <td>0.702692</td>
          <td>-1.297896</td>
          <td>one</td>
        </tr>
        <tr>
          <th>2</th>
          <td>6.549387</td>
          <td>-3.029808</td>
          <td>-1.845771</td>
          <td>-3.833306</td>
          <td>one</td>
        </tr>
        <tr>
          <th>3</th>
          <td>9.769170</td>
          <td>-6.183956</td>
          <td>-2.372003</td>
          <td>-4.119950</td>
          <td>one</td>
        </tr>
        <tr>
          <th>4</th>
          <td>6.628186</td>
          <td>-3.129730</td>
          <td>-1.412142</td>
          <td>-3.611312</td>
          <td>one</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>245</th>
          <td>7.033214</td>
          <td>-2.686300</td>
          <td>-2.319988</td>
          <td>-4.617525</td>
          <td>two</td>
        </tr>
        <tr>
          <th>246</th>
          <td>-8.605161</td>
          <td>5.013764</td>
          <td>3.277691</td>
          <td>5.560217</td>
          <td>two</td>
        </tr>
        <tr>
          <th>247</th>
          <td>11.574064</td>
          <td>-6.242021</td>
          <td>-3.464603</td>
          <td>-6.354748</td>
          <td>two</td>
        </tr>
        <tr>
          <th>248</th>
          <td>3.751013</td>
          <td>0.564987</td>
          <td>-1.655431</td>
          <td>-3.452477</td>
          <td>two</td>
        </tr>
        <tr>
          <th>249</th>
          <td>6.737896</td>
          <td>-2.744247</td>
          <td>-2.385954</td>
          <td>-4.576597</td>
          <td>two</td>
        </tr>
      </tbody>
    </table>
    <p>250 rows × 5 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 60-65

Explore the data
----------------

The parallel coordinate plot is a good place to start.  The differences in mean and variance across some
features is clear.

.. GENERATED FROM PYTHON SOURCE LINES 65-69

.. code-block:: default


    fig = plot_parallel_coordinates(x, color='group')
    fig






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 70-74

Explore the Principal Components
--------------------------------

Note that in the next plot, the data is colored by group, but the loading vectors are for the entire dataset.

.. GENERATED FROM PYTHON SOURCE LINES 74-79

.. code-block:: default


    fig = plot_principal_components(plot_3d=False, x=x.drop(columns=['group']), color=x['group'])
    fig.update_layout(height=800)
    fig






.. raw:: html

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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 85-86

And finally, by standardising the input data prior to PCA analysis allows the correlation circle to be shown by group.

.. GENERATED FROM PYTHON SOURCE LINES 86-91

.. code-block:: default

    fig = plot_correlation_circle(x=x.drop(columns=['group']), color=x['group'])
    fig.update_layout(height=800, width=800)
    plotly.io.show(fig)





.. raw:: html
    :file: images/sphx_glr_compare_datasets_001.html






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

   **Total running time of the script:** ( 0 minutes  0.344 seconds)


.. _sphx_glr_download_auto_examples_compare_datasets.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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