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A demo of the Spectral Co-Clustering algorithm#

This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clustering algorithm.

The dataset is generated using the make_biclusters function, which creates a matrix of small values and implants bicluster with large values. The rows and columns are then shuffled and passed to the Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to make biclusters contiguous shows how accurately the algorithm found the biclusters.

Original dataset
Shuffled dataset
After biclustering; rearranged to show biclusters

consensus score: 1.000

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importnumpyasnp
frommatplotlibimport pyplot as plt
fromsklearn.clusterimport SpectralCoclustering
fromsklearn.datasetsimport make_biclusters
fromsklearn.metricsimport consensus_score
data, rows, columns = make_biclusters (
 shape=(300, 300), n_clusters=5, noise=5, shuffle=False, random_state=0
)
plt.matshow (data, cmap=plt.cm.Blues)
plt.title ("Original dataset")
# shuffle clusters
rng = np.random.RandomState (0)
row_idx = rng.permutation(data.shape[0])
col_idx = rng.permutation(data.shape[1])
data = data[row_idx][:, col_idx]
plt.matshow (data, cmap=plt.cm.Blues)
plt.title ("Shuffled dataset")
model = SpectralCoclustering (n_clusters=5, random_state=0)
model.fit(data)
score = consensus_score (model.biclusters_, (rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.3f}".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.show ()