A demo of the Spectral Biclustering algorithmΒΆ

This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spectral Biclustering algorithm.

The data is generated with the make_checkerboard function, then shuffled and passed to the Spectral Biclustering algorithm. The rows and columns of the shuffled matrix are rearranged to show the biclusters found by the algorithm.

The outer product of the row and column label vectors shows a representation of the checkerboard structure.

  • ../../_images/sphx_glr_plot_spectral_biclustering_001.png
  • ../../_images/sphx_glr_plot_spectral_biclustering_002.png
  • ../../_images/sphx_glr_plot_spectral_biclustering_003.png
  • ../../_images/sphx_glr_plot_spectral_biclustering_004.png


consensus score: 1.0


# Author: Kemal Eren <kemal@kemaleren.com>
# License: BSD 3 clause

import numpy as np
from matplotlib import pyplot as plt

from sklearn.datasets import make_checkerboard
from sklearn.datasets import samples_generator as sg
from sklearn.cluster.bicluster import SpectralBiclustering
from sklearn.metrics import consensus_score

n_clusters = (4, 3)
data, rows, columns = make_checkerboard(
    shape=(300, 300), n_clusters=n_clusters, noise=10,
    shuffle=False, random_state=0)

plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")

data, row_idx, col_idx = sg._shuffle(data, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")

model = SpectralBiclustering(n_clusters=n_clusters, method='log',
score = consensus_score(model.biclusters_,
                        (rows[:, row_idx], columns[:, col_idx]))

print("consensus score: {:.1f}".format(score))

fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]

plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")

plt.matshow(np.outer(np.sort(model.row_labels_) + 1,
                     np.sort(model.column_labels_) + 1),
plt.title("Checkerboard structure of rearranged data")


Total running time of the script: ( 0 minutes 0.957 seconds)

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