.. _sphx_glr_auto_examples_bicluster_plot_spectral_biclustering.py: ============================================= A demo of the Spectral Biclustering algorithm ============================================= 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. .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_001.png :scale: 47 * .. image:: /auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_002.png :scale: 47 * .. image:: /auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_003.png :scale: 47 * .. image:: /auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_004.png :scale: 47 .. rst-class:: sphx-glr-script-out Out:: consensus score: 1.0 | .. code-block:: python print(__doc__) # Author: Kemal Eren # 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', random_state=0) model.fit(data) 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), cmap=plt.cm.Blues) plt.title("Checkerboard structure of rearranged data") plt.show() **Total running time of the script:** (0 minutes 0.740 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_spectral_biclustering.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_spectral_biclustering.ipynb `