Synthetic datasets Generation

This project contains the code to generate synthetic classification datasets. They can be useful as illustrative examples to analyze the performance of certain algorithms.

Currently the following datasets are available

• P2 dataset [1]
• Circle square [2]
• Banana [3]
• Banana 2 [4]

We also provide functions to plot the decision boundary of classification algorithms.

Installation:

The package can be installed using pip:

pip install git+https://github.com/Menelau/synthetic_datasets

Dependencies:

The code is tested to work with Python 3.5 and 3.6. The dependency requirements is only: numpy

matplotlib and scikit-learn are only needed to run the different examples.

• numpy
• matplotlib (only for plotting the data)
• scikit-learn (required only to run the examples)

These dependencies are automatically installed using the pip commands above.

Examples:

In this example we plot the P2 dataset with its decision border.

import matplotlib.pyplot as plt
from plot_datasets import plot_decision_P2, plot_dataset
from synthetic_datasets import generate_p2

X, y = generate_p2([1000, 1000])
title = ('P2 Dataset')
ax = plot_dataset(X, y, title=title)
plot_decision_P2(ax)

plt.savefig('ExampleP2.pdf', format='pdf')
plt.show()

In this example we plot the four datasets as well as the decision border of a trained Support Vector Machine (SVM).

from plot_datasets import plot_dataset, plot_classifier_decision
from synthetic_datasets import generate_p2, generate_circle_square, generate_banana, generate_banana2
import matplotlib.pyplot as plt
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split

# Set-up 2x2 grid for plotting.
fig, sub = plt.subplots(2, 2)
plt.subplots_adjust(wspace=0.4, hspace=0.4)

X_P2, y_P2 = generate_p2([1000, 1000])
X_cs, y_cs = generate_circle_square([1000, 1000])
X_banana, y_banana = generate_banana([1000, 1000])
X_banana2, y_banana2 = generate_banana2([1000, 1000])
X_list = list([X_P2, X_cs, X_banana, X_banana2])
y_list = list([y_P2, y_cs, y_banana, y_banana2])

# title for the plots
titles = ('P2 Dataset',
          'Circle Square Dataset',
          'Banana Dataset',
          'Banana 2 Dataset')

for X, y, title, ax in zip(X_list, y_list, titles, sub.flatten()):

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
    svm = SVC()
    svm.fit(X_train, y_train)
    plot_classifier_decision(ax, svm, X)
    plot_dataset(X, y, ax, title)
    ax.set_xlim(np.min(X[:, 0]), np.max(X[:, 0]))
    ax.set_ylim(np.min(X[:, 1]), np.max(X[:, 1]))

plt.savefig('Example.pdf', format='pdf')
plt.show()

References

[1]	:G. Valentini, An experimental bias-variance analysis of svm ensembles based on resampling techniques, IEEE Transactions on Systems, Man, and Cybernetics, Part B 35 (2005) 1252–1271.

[2]	: P. Henniges, E. Granger, R. Sabourin, Factors of overtraining with fuzzy artmap neural networks, International Joint Conference on Neural Networks (2005) 1075–1080.

[3]	: R.P.W. Duin, P. Juszczak, D.de Ridder, P. Paclik, E. Pekalska, D.M.Tax, Prtools, a matlab toolbox for pattern recognition, 2004. URL 〈http://www.prtools.org〉.

[4]	: Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2004.