## File: plot_spectral_coclustering.py

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scikit-learn 0.23.2-5
 `12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758` ``````""" ============================================== 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. """ print(__doc__) # Author: Kemal Eren # License: BSD 3 clause import numpy as np from matplotlib import pyplot as plt from sklearn.datasets import make_biclusters from sklearn.cluster 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") # 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() ``````