57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
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"""
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==============================================
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A demo of the Spectral Co-Clustering algorithm
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==============================================
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This example demonstrates how to generate a dataset and bicluster it
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using the Spectral Co-Clustering algorithm.
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The dataset is generated using the ``make_biclusters`` function, which
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creates a matrix of small values and implants bicluster with large
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values. The rows and columns are then shuffled and passed to the
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Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to
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make biclusters contiguous shows how accurately the algorithm found
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the biclusters.
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"""
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# Author: Kemal Eren <kemal@kemaleren.com>
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# License: BSD 3 clause
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import numpy as np
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from matplotlib import pyplot as plt
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from sklearn.cluster import SpectralCoclustering
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from sklearn.datasets import make_biclusters
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from sklearn.metrics import consensus_score
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data, rows, columns = make_biclusters(
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shape=(300, 300), n_clusters=5, noise=5, shuffle=False, random_state=0
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)
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plt.matshow(data, cmap=plt.cm.Blues)
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plt.title("Original dataset")
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# shuffle clusters
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rng = np.random.RandomState(0)
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row_idx = rng.permutation(data.shape[0])
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col_idx = rng.permutation(data.shape[1])
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data = data[row_idx][:, col_idx]
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plt.matshow(data, cmap=plt.cm.Blues)
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plt.title("Shuffled dataset")
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model = SpectralCoclustering(n_clusters=5, random_state=0)
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model.fit(data)
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score = consensus_score(model.biclusters_, (rows[:, row_idx], columns[:, col_idx]))
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print("consensus score: {:.3f}".format(score))
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fit_data = data[np.argsort(model.row_labels_)]
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fit_data = fit_data[:, np.argsort(model.column_labels_)]
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plt.matshow(fit_data, cmap=plt.cm.Blues)
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plt.title("After biclustering; rearranged to show biclusters")
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plt.show()
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