光谱共聚类算法演示#

此示例演示如何生成数据集并使用光谱共聚类算法对其进行双聚类。

数据集是使用make_biclusters函数生成的，该函数创建一个小值的矩阵并植入具有大值的双聚类。然后对行和列进行混洗，并传递给光谱共聚类算法。重新排列混洗后的矩阵以使双聚类连续，显示了算法查找双聚类的准确性。

consensus score: 1.000

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import numpy as np
from matplotlib import pyplot as plt

from sklearn.cluster import SpectralCoclustering
from sklearn.datasets import make_biclusters
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()

脚本总运行时间：（0分钟0.374秒）