6.3 Agglomerative Clustering.py 文件源码

def test_AgglomerativeClustering_linkage(*data):
    '''
    test the performance with different linkages
    :param data:  data, target
    :return: None
    '''
    X,labels_true=data
    nums=range(1,50)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)

    linkages=['ward','complete','average']
    markers="+o*"
    for i, linkage in enumerate(linkages):
        ARIs=[]
        for num in nums:
            clst=cluster.AgglomerativeClustering(n_clusters=num,linkage=linkage)
            predicted_labels=clst.fit_predict(X)
            ARIs.append(adjusted_rand_score(labels_true,predicted_labels))
        ax.plot(nums,ARIs,marker=markers[i],label="linkage:{0}".format(linkage))

    ax.set_xlabel("n_clusters")
    ax.set_ylabel("ARI")
    ax.legend(loc="best")
    fig.suptitle("AgglomerativeClustering")
    plt.show()