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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_dataset_use_cases_plot_iris.py>`
        to download the full example code

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

.. _sphx_glr_examples_dataset_use_cases_plot_iris.py:


Iris dataset
============

Presentation
------------

This is one of the best known dataset
to be found in the machine learning literature.

It was introduced by the statistician Ronald Fisher
in his 1936 paper "The use of multiple measurements in taxonomic problems",
Annals of Eugenics. 7 (2): 179–188.

It contains 150 instances of iris plants:

- 50 Iris Setosa,
- 50 Iris Versicolour,
- 50 Iris Virginica.

Each instance is characterized by:

- its sepal length in cm,
- its sepal width in cm,
- its petal length in cm,
- its petal width in cm.

This dataset can be used for either clustering purposes
or classification ones.

.. GENERATED FROM PYTHON SOURCE LINES 52-65

.. code-block:: default

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_benchmark_dataset
    from gemseo.post.dataset.andrews_curves import AndrewsCurves
    from gemseo.post.dataset.parallel_coordinates import ParallelCoordinates
    from gemseo.post.dataset.radviz import Radar
    from gemseo.post.dataset.scatter_plot_matrix import ScatterMatrix
    from numpy.random import choice

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 66-70

Load Iris dataset
-----------------
We can easily load this dataset
by means of the high-level function :func:`~gemseo.create_benchmark_dataset`:

.. GENERATED FROM PYTHON SOURCE LINES 70-73

.. code-block:: default


    iris = create_benchmark_dataset("IrisDataset")








.. GENERATED FROM PYTHON SOURCE LINES 74-75

and get some information about it

.. GENERATED FROM PYTHON SOURCE LINES 75-77

.. code-block:: default

    iris






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="4" halign="left">parameters</th>
          <th>labels</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>sepal_length</th>
          <th>sepal_width</th>
          <th>petal_length</th>
          <th>petal_width</th>
          <th>specy</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>5.1</td>
          <td>3.5</td>
          <td>1.4</td>
          <td>0.2</td>
          <td>0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>4.9</td>
          <td>3.0</td>
          <td>1.4</td>
          <td>0.2</td>
          <td>0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>4.7</td>
          <td>3.2</td>
          <td>1.3</td>
          <td>0.2</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4.6</td>
          <td>3.1</td>
          <td>1.5</td>
          <td>0.2</td>
          <td>0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5.0</td>
          <td>3.6</td>
          <td>1.4</td>
          <td>0.2</td>
          <td>0</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>145</th>
          <td>6.7</td>
          <td>3.0</td>
          <td>5.2</td>
          <td>2.3</td>
          <td>2</td>
        </tr>
        <tr>
          <th>146</th>
          <td>6.3</td>
          <td>2.5</td>
          <td>5.0</td>
          <td>1.9</td>
          <td>2</td>
        </tr>
        <tr>
          <th>147</th>
          <td>6.5</td>
          <td>3.0</td>
          <td>5.2</td>
          <td>2.0</td>
          <td>2</td>
        </tr>
        <tr>
          <th>148</th>
          <td>6.2</td>
          <td>3.4</td>
          <td>5.4</td>
          <td>2.3</td>
          <td>2</td>
        </tr>
        <tr>
          <th>149</th>
          <td>5.9</td>
          <td>3.0</td>
          <td>5.1</td>
          <td>1.8</td>
          <td>2</td>
        </tr>
      </tbody>
    </table>
    <p>150 rows × 5 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 78-81

Manipulate the dataset
----------------------
We randomly select 10 samples to display.

.. GENERATED FROM PYTHON SOURCE LINES 81-83

.. code-block:: default

    samples = choice(len(iris), size=10, replace=False)








.. GENERATED FROM PYTHON SOURCE LINES 84-86

We can easily access the 10 samples previously selected,
either globally

.. GENERATED FROM PYTHON SOURCE LINES 86-88

.. code-block:: default

    data = iris.get_view(indices=samples)








.. GENERATED FROM PYTHON SOURCE LINES 89-90

or only the parameters:

.. GENERATED FROM PYTHON SOURCE LINES 90-92

.. code-block:: default

    iris.get_view(group_names=iris.PARAMETER_GROUP, indices=samples)






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="4" halign="left">parameters</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>sepal_length</th>
          <th>sepal_width</th>
          <th>petal_length</th>
          <th>petal_width</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>14</th>
          <td>5.8</td>
          <td>4.0</td>
          <td>1.2</td>
          <td>0.2</td>
        </tr>
        <tr>
          <th>22</th>
          <td>4.6</td>
          <td>3.6</td>
          <td>1.0</td>
          <td>0.2</td>
        </tr>
        <tr>
          <th>114</th>
          <td>5.8</td>
          <td>2.8</td>
          <td>5.1</td>
          <td>2.4</td>
        </tr>
        <tr>
          <th>10</th>
          <td>5.4</td>
          <td>3.7</td>
          <td>1.5</td>
          <td>0.2</td>
        </tr>
        <tr>
          <th>86</th>
          <td>6.7</td>
          <td>3.1</td>
          <td>4.7</td>
          <td>1.5</td>
        </tr>
        <tr>
          <th>43</th>
          <td>5.0</td>
          <td>3.5</td>
          <td>1.6</td>
          <td>0.6</td>
        </tr>
        <tr>
          <th>35</th>
          <td>5.0</td>
          <td>3.2</td>
          <td>1.2</td>
          <td>0.2</td>
        </tr>
        <tr>
          <th>106</th>
          <td>4.9</td>
          <td>2.5</td>
          <td>4.5</td>
          <td>1.7</td>
        </tr>
        <tr>
          <th>2</th>
          <td>4.7</td>
          <td>3.2</td>
          <td>1.3</td>
          <td>0.2</td>
        </tr>
        <tr>
          <th>118</th>
          <td>7.7</td>
          <td>2.6</td>
          <td>6.9</td>
          <td>2.3</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 93-94

or only the labels:

.. GENERATED FROM PYTHON SOURCE LINES 94-96

.. code-block:: default

    iris.get_view(group_names="labels", indices=samples)






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th>labels</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>specy</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>14</th>
          <td>0</td>
        </tr>
        <tr>
          <th>22</th>
          <td>0</td>
        </tr>
        <tr>
          <th>114</th>
          <td>2</td>
        </tr>
        <tr>
          <th>10</th>
          <td>0</td>
        </tr>
        <tr>
          <th>86</th>
          <td>1</td>
        </tr>
        <tr>
          <th>43</th>
          <td>0</td>
        </tr>
        <tr>
          <th>35</th>
          <td>0</td>
        </tr>
        <tr>
          <th>106</th>
          <td>2</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0</td>
        </tr>
        <tr>
          <th>118</th>
          <td>2</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 97-101

Plot the dataset
----------------
Lastly, we can plot the dataset in various ways. We will note that the
samples are colored according to their labels.

.. GENERATED FROM PYTHON SOURCE LINES 103-109

Plot scatter matrix
~~~~~~~~~~~~~~~~~~~
We can use the :class:`.ScatterMatrix` plot where each non-diagonal block
represents the samples according to the x- and y- coordinates names
while the diagonal ones approximate the probability distributions of the
variables, using either an histogram or a kernel-density estimator.

.. GENERATED FROM PYTHON SOURCE LINES 109-111

.. code-block:: default

    ScatterMatrix(iris, classifier="specy", kde=True).execute(save=False, show=True)




.. image-sg:: /examples/dataset/use_cases/images/sphx_glr_plot_iris_001.png
   :alt: plot iris
   :srcset: /examples/dataset/use_cases/images/sphx_glr_plot_iris_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    [<Figure size 640x480 with 16 Axes>]



.. GENERATED FROM PYTHON SOURCE LINES 112-119

Plot parallel coordinates
~~~~~~~~~~~~~~~~~~~~~~~~~
We can use the
:class:`~gemseo.post.dataset.parallel_coordinates.ParallelCoordinates` plot,
a.k.a. cowebplot, where each samples
is represented by a continuous straight line in pieces whose nodes are
indexed by the variables names and measure the variables values.

.. GENERATED FROM PYTHON SOURCE LINES 119-121

.. code-block:: default

    ParallelCoordinates(iris, "specy").execute(save=False, show=True)




.. image-sg:: /examples/dataset/use_cases/images/sphx_glr_plot_iris_002.png
   :alt: plot iris
   :srcset: /examples/dataset/use_cases/images/sphx_glr_plot_iris_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    [<Figure size 640x480 with 1 Axes>]



.. GENERATED FROM PYTHON SOURCE LINES 122-128

Plot Andrews curves
~~~~~~~~~~~~~~~~~~~
We can use the :class:`.AndrewsCurves` plot
which can be viewed as a smooth
version of the parallel coordinates. Each sample is represented by a curve
and if there is structure in data, it may be visible in the plot.

.. GENERATED FROM PYTHON SOURCE LINES 128-130

.. code-block:: default

    AndrewsCurves(iris, "specy").execute(save=False, show=True)




.. image-sg:: /examples/dataset/use_cases/images/sphx_glr_plot_iris_003.png
   :alt: plot iris
   :srcset: /examples/dataset/use_cases/images/sphx_glr_plot_iris_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    [<Figure size 640x480 with 1 Axes>]



.. GENERATED FROM PYTHON SOURCE LINES 131-134

Plot Radar
~~~~~~~~~~
We can use the :class:`.Radar` plot

.. GENERATED FROM PYTHON SOURCE LINES 134-135

.. code-block:: default

    Radar(iris, "specy").execute(save=False, show=True)



.. image-sg:: /examples/dataset/use_cases/images/sphx_glr_plot_iris_004.png
   :alt: plot iris
   :srcset: /examples/dataset/use_cases/images/sphx_glr_plot_iris_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    [<Figure size 640x480 with 1 Axes>]




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

   **Total running time of the script:** (0 minutes 1.420 seconds)


.. _sphx_glr_download_examples_dataset_use_cases_plot_iris.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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