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.
Out:
1 | consensus score: 1.000 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | print(__doc__)# Author: Kemal Eren <kemal@kemaleren.com># License: BSD 3 clauseimport numpy as npfrom matplotlib import pyplot as pltfrom sklearn.datasets import make_biclustersfrom sklearn.datasets import samples_generator as sgfrom sklearn.cluster.bicluster import SpectralCoclusteringfrom sklearn.metrics import consensus_scoredata, 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 0.216 seconds)
Download Python source code:
plot_spectral_coclustering.py
Download IPython notebook:
plot_spectral_coclustering.ipynb
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