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.

from __future__ import absolute_import, division, print_function, unicode_literals

from future import standard_library
from numpy.random import choice

from gemseo.api import configure_logger, load_dataset

configure_logger()

standard_library.install_aliases()

Load Iris dataset

We can easily load this dataset by means of the load_dataset() function of the API:

iris = load_dataset("IrisDataset")

and get some information about it

print(iris)

Out:

Iris
| Number of samples: 150
| Number of variables: 5
| Variables names and sizes by group:
| - parameters: sepal_length (1), sepal_width (1), petal_length (1), petal_width (1)
| - labels: specy (1)
| Number of dimensions (total = 5) by group:
| - parameters: 4
| - labels: 1

Manipulate the dataset

We randomly select 10 samples to display.

shown_samples = choice(iris.length, size=10, replace=False)

If the pandas library is installed, we can export the iris dataset to a dataframe and print(it.

dataframe = iris.export_to_dataframe()
print(dataframe)

Out:

      parameters                                      labels
    sepal_length sepal_width petal_length petal_width  specy
               0           0            0           0      0
0            5.1         3.5          1.4         0.2    0.0
1            4.9         3.0          1.4         0.2    0.0
2            4.7         3.2          1.3         0.2    0.0
3            4.6         3.1          1.5         0.2    0.0
4            5.0         3.6          1.4         0.2    0.0
..           ...         ...          ...         ...    ...
145          6.7         3.0          5.2         2.3    2.0
146          6.3         2.5          5.0         1.9    2.0
147          6.5         3.0          5.2         2.0    2.0
148          6.2         3.4          5.4         2.3    2.0
149          5.9         3.0          5.1         1.8    2.0

[150 rows x 5 columns]

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

data = iris.get_all_data(False)
print(data[0][shown_samples, :])

Out:

[[5.8 4.  1.2 0.2 0. ]
 [5.1 2.5 3.  1.1 1. ]
 [6.6 3.  4.4 1.4 1. ]
 [5.4 3.9 1.3 0.4 0. ]
 [7.9 3.8 6.4 2.  2. ]
 [6.3 3.3 4.7 1.6 1. ]
 [6.9 3.1 5.1 2.3 2. ]
 [5.1 3.8 1.9 0.4 0. ]
 [4.7 3.2 1.6 0.2 0. ]
 [6.9 3.2 5.7 2.3 2. ]]

or only the parameters:

parameters = iris.get_data_by_group("parameters")
print(parameters[shown_samples, :])

Out:

[[5.8 4.  1.2 0.2]
 [5.1 2.5 3.  1.1]
 [6.6 3.  4.4 1.4]
 [5.4 3.9 1.3 0.4]
 [7.9 3.8 6.4 2. ]
 [6.3 3.3 4.7 1.6]
 [6.9 3.1 5.1 2.3]
 [5.1 3.8 1.9 0.4]
 [4.7 3.2 1.6 0.2]
 [6.9 3.2 5.7 2.3]]

or only the labels:

labels = iris.get_data_by_group("labels")
print(labels[shown_samples, :])

Out:

[[0.]
 [1.]
 [1.]
 [0.]
 [2.]
 [1.]
 [2.]
 [0.]
 [0.]
 [2.]]

Plot the dataset

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

Plot scatter matrix

We can use the 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.

iris.plot("ScatterMatrix", classifier="specy", kde=True)

Out:

<gemseo.post.dataset.scatter_plot_matrix.ScatterMatrix object at 0x7fc29e2dc8e0>

Plot parallel coordinates

We can use the 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.

iris.plot("ParallelCoordinates", classifier="specy")

Out:

<gemseo.post.dataset.parallel_coordinates.ParallelCoordinates object at 0x7fc29de975b0>

Plot Andrews curves

We can use the 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.

iris.plot("AndrewsCurves", classifier="specy")

Out:

<gemseo.post.dataset.andrews_curves.AndrewsCurves object at 0x7fc29e2dc790>

Plot Radar

We can use the Radar plot

iris.plot("Radar", classifier="specy")

Out:

<gemseo.post.dataset.radviz.Radar object at 0x7fc29da599d0>