Multiview Spherical KMeans Tutorial

This tutorial demonstrates the multiview spherical k-means algorithm on 2 views of the UCI multiview digits dataset.

# License: MIT

from mvlearn.datasets import load_UCImultifeature
from mvlearn.cluster import MultiviewSphericalKMeans
from sklearn.manifold import TSNE
from sklearn.metrics import normalized_mutual_info_score as nmi_score
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')  # Ignore warnings

# Load in UCI digits multiple feature dataset as an example

RANDOM_SEED = 5
# Load dataset along with labels for digits 0 through 4
n_class = 5
Xs, labels = load_UCImultifeature(
    select_labeled=list(range(n_class)), views=[0, 1])


# Creating a function to display data and the results of clustering
def display_plots(pre_title, data, labels):
    # plot the views
    fig, ax = plt.subplots(1, 2, figsize=(14, 5))
    dot_size = 10
    ax[0].scatter(data[0][:, 0], data[0][:, 1], c=labels, s=dot_size)
    ax[0].set_title(pre_title + ' View 1')
    ax[0].axes.get_xaxis().set_visible(False)
    ax[0].axes.get_yaxis().set_visible(False)

    ax[1].scatter(data[1][:, 0], data[1][:, 1], c=labels, s=dot_size)
    ax[1].set_title(pre_title + ' View 2')
    ax[1].axes.get_xaxis().set_visible(False)
    ax[1].axes.get_yaxis().set_visible(False)

    plt.show()

Multiview spherical KMeans clustering on 2 views

Here we will demonstrate the performance of the multiview spherical kmeans clustering. We will evaluate the purity of the resulting clusters with respect to the class labels using the normalized mutual information metric.

Use the MultiviewSphericalKMeans instance to cluster the data

m_kmeans = MultiviewSphericalKMeans(
    n_clusters=n_class, random_state=RANDOM_SEED)

m_clusters = m_kmeans.fit_predict(Xs)

# Compute nmi between true class labels and multiview cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('multiview NMI Score: {0:.3f}\n'.format(m_nmi))

Out:

multiview NMI Score: 0.819

Multiview spectral clustering results and the true clusters

We will display the clustering results of the multiview kmeans clustering algorithm below, along with the true class labels.

# Running TSNE to display clustering results via low dimensional embedding
tsne = TSNE()
new_data_1 = tsne.fit_transform(Xs[0])
new_data_2 = tsne.fit_transform(Xs[1])
new_data = [new_data_1, new_data_2]

display_plots('True Labels', new_data, labels)
display_plots('multiview KMeans Clusters', new_data, m_clusters)

• 
• 

Multiview spherical KMeans clustering different parameters

Here we will follow a similar procedure as before, but we will be using a different configuration of parameters for multiview Spherical KMeans Clustering.

# Use the MultiviewSphericalKMeans instance to cluster the data
m_kmeans = MultiviewSphericalKMeans(
    n_clusters=n_class, n_init=10, max_iter=6, patience=2,
    random_state=RANDOM_SEED)
m_clusters = m_kmeans.fit_predict(Xs)

# Compute nmi between true class labels and multiview cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('multiview NMI Score: {0:.3f}\n'.format(m_nmi))

Out:

multiview NMI Score: 0.575