.. 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 52-68

.. code-block:: Python


    from __future__ import annotations

    from numpy.random import default_rng

    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

    configure_logger()

    rng = default_rng(1)








.. GENERATED FROM PYTHON SOURCE LINES 69-73

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 73-76

.. code-block:: Python


    iris = create_benchmark_dataset("IrisDataset")








.. GENERATED FROM PYTHON SOURCE LINES 77-78

and get some information about it

.. GENERATED FROM PYTHON SOURCE LINES 78-80

.. code-block:: Python

    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 81-84

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

.. GENERATED FROM PYTHON SOURCE LINES 84-86

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 87-89

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

.. GENERATED FROM PYTHON SOURCE LINES 89-91

.. code-block:: Python

    data = iris.get_view(indices=samples)








.. GENERATED FROM PYTHON SOURCE LINES 92-93

or only the parameters:

.. GENERATED FROM PYTHON SOURCE LINES 93-95

.. code-block:: Python

    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>46</th>
          <td>5.1</td>
          <td>3.8</td>
          <td>1.6</td>
          <td>0.2</td>
        </tr>
        <tr>
          <th>66</th>
          <td>5.6</td>
          <td>3.0</td>
          <td>4.5</td>
          <td>1.5</td>
        </tr>
        <tr>
          <th>120</th>
          <td>6.9</td>
          <td>3.2</td>
          <td>5.7</td>
          <td>2.3</td>
        </tr>
        <tr>
          <th>5</th>
          <td>5.4</td>
          <td>3.9</td>
          <td>1.7</td>
          <td>0.4</td>
        </tr>
        <tr>
          <th>140</th>
          <td>6.7</td>
          <td>3.1</td>
          <td>5.6</td>
          <td>2.4</td>
        </tr>
        <tr>
          <th>72</th>
          <td>6.3</td>
          <td>2.5</td>
          <td>4.9</td>
          <td>1.5</td>
        </tr>
        <tr>
          <th>21</th>
          <td>5.1</td>
          <td>3.7</td>
          <td>1.5</td>
          <td>0.4</td>
        </tr>
        <tr>
          <th>107</th>
          <td>7.3</td>
          <td>2.9</td>
          <td>6.3</td>
          <td>1.8</td>
        </tr>
        <tr>
          <th>136</th>
          <td>6.3</td>
          <td>3.4</td>
          <td>5.6</td>
          <td>2.4</td>
        </tr>
        <tr>
          <th>37</th>
          <td>4.9</td>
          <td>3.6</td>
          <td>1.4</td>
          <td>0.1</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 96-97

or only the labels:

.. GENERATED FROM PYTHON SOURCE LINES 97-99

.. code-block:: Python

    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>46</th>
          <td>0</td>
        </tr>
        <tr>
          <th>66</th>
          <td>1</td>
        </tr>
        <tr>
          <th>120</th>
          <td>2</td>
        </tr>
        <tr>
          <th>5</th>
          <td>0</td>
        </tr>
        <tr>
          <th>140</th>
          <td>2</td>
        </tr>
        <tr>
          <th>72</th>
          <td>1</td>
        </tr>
        <tr>
          <th>21</th>
          <td>0</td>
        </tr>
        <tr>
          <th>107</th>
          <td>2</td>
        </tr>
        <tr>
          <th>136</th>
          <td>2</td>
        </tr>
        <tr>
          <th>37</th>
          <td>0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 100-104

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 106-112

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 112-114

.. code-block:: Python

    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 115-122

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 122-124

.. code-block:: Python

    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 125-131

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 131-133

.. code-block:: Python

    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 134-137

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

.. GENERATED FROM PYTHON SOURCE LINES 137-138

.. code-block:: Python

    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

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/stable/lib/python3.9/site-packages/gemseo/datasets/dataset.py:416: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
     'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
     'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
      self.loc[

    [<Figure size 640x480 with 1 Axes>]




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

   **Total running time of the script:** (0 minutes 1.532 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-jupyter

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

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

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


.. only:: html

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

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