adjusted rand index和adjusted mutual information score

import matplotlib.pyplot as plt import numpy as np from sklearn import metrics def uniform_labelings_scores(score_func, n_samples, n_clusters_range, fixed_n_classes=None, n_runs=5, seed=42): random_labels = np.random.RandomState(seed).random_integers scores = np.zeros((len(n_clusters_range), n_runs)) for i, k in enumerate(n_clusters_range): for j in range(n_runs): if fixed_n_classes is None: labels_a = random_labels(low=1, high=k, size=n_samples) else: labels_a = random_labels(low=1, high=fixed_n_classes, size=n_samples) labels_b = random_labels(low=1, high=k, size=n_samples) scores[i, j] = score_func(labels_a, labels_b) return scores score_funcs = [metrics.adjusted_rand_score, metrics.adjusted_mutual_info_score] n_samples = 100 n_clusters_range = map(int, np.linspace(2, 100, 10)) plots = [] names = [] for score_func in score_funcs: scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range, fixed_n_classes=10) plots.append(plt.errorbar(n_clusters_range, np.mean(scores, axis=1), scores.std(axis=1))[0]) names.append(score_func.__name__) plt.legend(plots, names) plt.show()