.. _sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py:


==============================================
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.





.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_001.png
            :scale: 47

    *

      .. image:: /auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_002.png
            :scale: 47

    *

      .. image:: /auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_003.png
            :scale: 47


.. rst-class:: sphx-glr-script-out

 Out::

    consensus score: 1.000




|


.. code-block:: python

    print(__doc__)

    # 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_biclusters
    from sklearn.datasets import samples_generator as sg
    from sklearn.cluster.bicluster import SpectralCoclustering
    from sklearn.metrics import 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")

    data, row_idx, col_idx = sg._shuffle(data, random_state=0)
    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()

**Total running time of the script:** ( 0 minutes  0.111 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_spectral_coclustering.py <plot_spectral_coclustering.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_spectral_coclustering.ipynb <plot_spectral_coclustering.ipynb>`

.. rst-class:: sphx-glr-signature

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