Loading and Viewing the UCI Multiple Features Dataset

In this tutorial we demonstrate how to load and quickly visualize the Multiple Features Dataset [1] from the UCI repository, which is available in mvlearn. This dataset can be a good tool for analyzing the effectiveness of multiview algorithms. It contains 6 views of handwritten digit images, thus allowing for analysis of multiview algorithms in multiclass or unsupervised tasks.

[1] M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386

# License: MIT

from mvlearn.datasets import load_UCImultifeature
from mvlearn.plotting import quick_visualize

Load the data and labels

Here We can load the entire dataset (all 10 digits). Then, visualize in 2D.

# Load entire dataset
full_data, full_labels = load_UCImultifeature()

print("Full Dataset\n")
print("Views = " + str(len(full_data)))
print("First view shape = " + str(full_data[0].shape))
print("Labels shape = " + str(full_labels.shape))

quick_visualize(full_data, labels=full_labels, title="10-class data")

Out:

Full Dataset

Views = 6
First view shape = (2000, 76)
Labels shape = (2000,)

Load only 2 Classes of the Data

If we want only a binary classification setup, we can choose to only load 2 of the classes. Also, we can shuffle the data and set the seed for reproducibility. Then, we visualize in 2D.

partial_data, partial_labels = load_UCImultifeature(
    select_labeled=[0, 1], shuffle=True, random_state=42)

print("\n\nPartial Dataset (only 0's and 1's)\n")
print("Views = " + str(len(partial_data)))
print("First view shape = " + str(partial_data[0].shape))
print("Labels shape = " + str(partial_labels.shape))

quick_visualize(partial_data, labels=partial_labels, title="2-class data")

Out:

Partial Dataset (only 0's and 1's)

Views = 6
First view shape = (400, 76)
Labels shape = (400,)