注意：單擊此處https://urlify.cn/3iAzUr下載完整的示例代碼，或通過Binder在瀏覽器中運行此示例

下圖說明了聚類數量和樣本數量對各種聚類性能評估度量指標的影響。未調整的度量指標(例如V度量)顯示了聚類的數量與樣本數之間的依賴關系：隨機標記的平均V度量隨著聚類的數量越接近用于計算的樣本總數而顯著增加。針對ARI等偶然性度量指標進行調整后，對于任意數量的樣本和聚類，一些隨機方差(variations)均以0.0的平均得分為中心。因此，只有調整后的度量指標才能安全地用作共識指數(consensus index)，才能用來評估數據集中在各種重疊子樣本上給定k值時，聚類算法的平均穩定性。

輸出：

Computing adjusted_rand_score for 10 values of n_clusters and n_samples=100done in 0.050sComputing v_measure_score for 10 values of n_clusters and n_samples=100done in 0.068sComputing ami_score for 10 values of n_clusters and n_samples=100done in 0.356sComputing mutual_info_score for 10 values of n_clusters and n_samples=100done in 0.044sComputing adjusted_rand_score for 10 values of n_clusters and n_samples=1000done in 0.051sComputing v_measure_score for 10 values of n_clusters and n_samples=1000done in 0.064sComputing ami_score for 10 values of n_clusters and n_samples=1000done in 0.208sComputing mutual_info_score for 10 values of n_clusters and n_samples=1000done in 0.048s

print(__doc__)# 作者: Olivier Grisel # 許可證: BSD 3 clauseimport numpy as npimport matplotlib.pyplot as pltfrom time import timefrom sklearn import metricsdef uniform_labelings_scores(score_func, n_samples, n_clusters_range,                             fixed_n_classes=None, n_runs=5, seed=42):    """計算2個隨機均一聚類標簽的得分。???? 兩個隨機標簽中每個在n_clusters_range中的可能值都具有相同數量的聚類。???????? 當fixed_n_classes不為None時，第一個標簽被認為是具有固定類數量的真實類(ground truth class)。    """    random_labels = np.random.RandomState(seed).randint    scores = np.zeros((len(n_clusters_range), n_runs))    if fixed_n_classes is not None:        labels_a = random_labels(low=0, high=fixed_n_classes, size=n_samples)    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=0, high=k, size=n_samples)            labels_b = random_labels(low=0, high=k, size=n_samples)            scores[i, j] = score_func(labels_a, labels_b)    return scoresdef ami_score(U, V):    return metrics.adjusted_mutual_info_score(U, V)score_funcs = [    metrics.adjusted_rand_score,    metrics.v_measure_score,    ami_score,    metrics.mutual_info_score,]# 2個獨立的隨機聚類，具有相同的聚類數n_samples = 100n_clusters_range = np.linspace(2, n_samples, 10).astype(np.int)plt.figure(1)plots = []names = []for score_func in score_funcs:    print("Computing %s for %d values of n_clusters and n_samples=%d"          % (score_func.__name__, len(n_clusters_range), n_samples))    t0 = time()    scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range)    print("done in %0.3fs" % (time() - t0))    plots.append(plt.errorbar(        n_clusters_range, np.median(scores, axis=1), scores.std(axis=1))[0])    names.append(score_func.__name__)plt.title("Clustering measures for 2 random uniform labelings\n"          "with equal number of clusters")plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)plt.ylabel('Score value')plt.legend(plots, names)plt.ylim(bottom=-0.05, top=1.05)# 根據真實類標簽使用不同的n_clusters隨機標簽# 聚類數量固定n_samples = 1000n_clusters_range = np.linspace(2, 100, 10).astype(np.int)n_classes = 10plt.figure(2)plots = []names = []for score_func in score_funcs:    print("Computing %s for %d values of n_clusters and n_samples=%d"          % (score_func.__name__, len(n_clusters_range), n_samples))    t0 = time()    scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range,                                      fixed_n_classes=n_classes)    print("done in %0.3fs" % (time() - t0))    plots.append(plt.errorbar(        n_clusters_range, scores.mean(axis=1), scores.std(axis=1))[0])    names.append(score_func.__name__)plt.title("Clustering measures for random uniform labeling\n"          "against reference assignment with %d classes" % n_classes)plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)plt.ylabel('Score value')plt.ylim(bottom=-0.05, top=1.05)plt.legend(plots, names)plt.show()

腳本的總運行時間：(0分鐘1.225秒)估計的內存使用量： 8 MB下載Python源代碼: plot_adjusted_for_chance_measures.py下載Jupyter notebook源代碼: plot_adjusted_for_chance_measures.ipynb由Sphinx-Gallery生成的畫廊?

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