.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/dataset/plot_iris.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_dataset_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 52-66 .. code-block:: default from __future__ import annotations from gemseo.api import configure_logger from gemseo.api import load_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 matplotlib import pyplot as plt from numpy.random import choice configure_logger() .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 67-71 Load Iris dataset ----------------- We can easily load this dataset by means of the :meth:`~gemseo.api.load_dataset` function of the API: .. GENERATED FROM PYTHON SOURCE LINES 71-74 .. code-block:: default iris = load_dataset("IrisDataset") .. GENERATED FROM PYTHON SOURCE LINES 75-76 and get some information about it .. GENERATED FROM PYTHON SOURCE LINES 76-78 .. code-block:: default print(iris) .. rst-class:: sphx-glr-script-out .. code-block:: none Iris Number of samples: 150 Number of variables: 5 Variables names and sizes by group: labels: specy (1) parameters: petal_length (1), petal_width (1), sepal_length (1), sepal_width (1) Number of dimensions (total = 5) by group: labels: 1 parameters: 4 .. GENERATED FROM PYTHON SOURCE LINES 79-82 Manipulate the dataset ---------------------- We randomly select 10 samples to display. .. GENERATED FROM PYTHON SOURCE LINES 82-85 .. code-block:: default shown_samples = choice(iris.length, size=10, replace=False) .. GENERATED FROM PYTHON SOURCE LINES 86-88 If the pandas library is installed, we can export the iris dataset to a dataframe and print(it. .. GENERATED FROM PYTHON SOURCE LINES 88-91 .. code-block:: default dataframe = iris.export_to_dataframe() print(dataframe) .. rst-class:: sphx-glr-script-out .. code-block:: none parameters labels petal_length petal_width sepal_length sepal_width specy 0 0 0 0 0 0 1.4 0.2 5.1 3.5 0.0 1 1.4 0.2 4.9 3.0 0.0 2 1.3 0.2 4.7 3.2 0.0 3 1.5 0.2 4.6 3.1 0.0 4 1.4 0.2 5.0 3.6 0.0 .. ... ... ... ... ... 145 5.2 2.3 6.7 3.0 2.0 146 5.0 1.9 6.3 2.5 2.0 147 5.2 2.0 6.5 3.0 2.0 148 5.4 2.3 6.2 3.4 2.0 149 5.1 1.8 5.9 3.0 2.0 [150 rows x 5 columns] .. GENERATED FROM PYTHON SOURCE LINES 92-94 We can also easily access the 10 samples previously selected, either globally .. GENERATED FROM PYTHON SOURCE LINES 94-97 .. code-block:: default data = iris.get_all_data(False) print(data[0][shown_samples, :]) .. rst-class:: sphx-glr-script-out .. code-block:: none [[0. 5. 3.2 1.2 0.2] [2. 6.8 3.2 5.9 2.3] [1. 6. 2.2 4. 1. ] [2. 6. 3. 4.8 1.8] [1. 6. 3.4 4.5 1.6] [0. 4.9 3.1 1.5 0.2] [2. 7.3 2.9 6.3 1.8] [2. 6.2 3.4 5.4 2.3] [0. 5.4 3.4 1.7 0.2] [2. 5.8 2.7 5.1 1.9]] .. GENERATED FROM PYTHON SOURCE LINES 98-99 or only the parameters: .. GENERATED FROM PYTHON SOURCE LINES 99-102 .. code-block:: default parameters = iris.get_data_by_group("parameters") print(parameters[shown_samples, :]) .. rst-class:: sphx-glr-script-out .. code-block:: none [[5. 3.2 1.2 0.2] [6.8 3.2 5.9 2.3] [6. 2.2 4. 1. ] [6. 3. 4.8 1.8] [6. 3.4 4.5 1.6] [4.9 3.1 1.5 0.2] [7.3 2.9 6.3 1.8] [6.2 3.4 5.4 2.3] [5.4 3.4 1.7 0.2] [5.8 2.7 5.1 1.9]] .. GENERATED FROM PYTHON SOURCE LINES 103-104 or only the labels: .. GENERATED FROM PYTHON SOURCE LINES 104-107 .. code-block:: default labels = iris.get_data_by_group("labels") print(labels[shown_samples, :]) .. rst-class:: sphx-glr-script-out .. code-block:: none [[0.] [2.] [1.] [2.] [1.] [0.] [2.] [2.] [0.] [2.]] .. GENERATED FROM PYTHON SOURCE LINES 108-112 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 114-120 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 120-122 .. code-block:: default ScatterMatrix(iris, classifier="specy", kde=True).execute(save=False, show=False) .. image-sg:: /examples/dataset/images/sphx_glr_plot_iris_001.png :alt: plot iris :srcset: /examples/dataset/images/sphx_glr_plot_iris_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.1.0/lib/python3.9/site-packages/gemseo/post/dataset/scatter_plot_matrix.py:135: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared. sub_axes = scatter_matrix( [
] .. GENERATED FROM PYTHON SOURCE LINES 123-130 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 130-132 .. code-block:: default ParallelCoordinates(iris, "specy").execute(save=False, show=False) .. image-sg:: /examples/dataset/images/sphx_glr_plot_iris_002.png :alt: plot iris :srcset: /examples/dataset/images/sphx_glr_plot_iris_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [
] .. GENERATED FROM PYTHON SOURCE LINES 133-139 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 139-141 .. code-block:: default AndrewsCurves(iris, "specy").execute(save=False, show=False) .. image-sg:: /examples/dataset/images/sphx_glr_plot_iris_003.png :alt: plot iris :srcset: /examples/dataset/images/sphx_glr_plot_iris_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [
] .. GENERATED FROM PYTHON SOURCE LINES 142-145 Plot Radar ~~~~~~~~~~ We can use the :class:`.Radar` plot .. GENERATED FROM PYTHON SOURCE LINES 145-148 .. code-block:: default Radar(iris, "specy").execute(save=False, show=False) # Workaround for HTML rendering, instead of ``show=True`` plt.show() .. image-sg:: /examples/dataset/images/sphx_glr_plot_iris_004.png :alt: plot iris :srcset: /examples/dataset/images/sphx_glr_plot_iris_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.546 seconds) .. _sphx_glr_download_examples_dataset_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 ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_iris.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_