scikitlearn中聚类结果的标签值与原样本数据的对应

Myth丶恋晨 2022-02-01 09:09 860阅读 0赞

# 1 问题来源 #

在使用sklearn中的聚类算法过程中，得到的一般为聚类结果的标签值。例如，\[1,1,0,1,0,1,0,1,0,1,2,-1,-1,2,3,3,1,-1,-1\]这样的标签值，如何将聚类结果的\_labels值与原样本对应并打印输出，是亟待解决的问题。

# 2 源码实现 #

labels\_to\_original函数的功能是，将forclusterlist中的样本集按照labels中的标签值重新排序，得到按照类簇排列好的输出结果

# labels为聚类结果的标签值
        # forclusterlist为聚类所使用的样本集
        # 函数的功能是将forclusterlist中的样本集按照labels中的标签值重新排序，得到按照类簇排列好的输出结果
    def labels_to_original(labels, forclusterlist):
        assert len(labels) == len(forclusterlist)
        maxlabel = max(labels)
        numberlabel = [i for i in range(0, maxlabel + 1, 1)]
        numberlabel.append(-1)
        result = [[] for i in range(len(numberlabel))]
        for i in range(len(labels)):
            index = numberlabel.index(labels[i])
            result[index].append(forclusterlist[i])
        return result

# 3 使用实例 #

聚类的样本集：forclusterlist = \[“类0”,“类1”,“类2”,“噪声”,“类1”,“类2”,“类1”,“类2”,“类3”,“类1”,“类2”,“类1”,“噪声”,“类2”,“类2”,“噪声”\]  
聚类的标签值：labels = \[0,1,2,-1,1,2,1,2,3,1,2,1,-1,2,2,-1\]  
使用上述函数运行：

forclusterlist = ["类0","类1","类2","噪声","类1","类2","类1","类2","类3","类1","类2","类1","噪声","类2","类2","噪声"]
    labels = [0,1,2,-1,1,2,1,2,3,1,2,1,-1,2,2,-1]
    result = labels_to_original(labels, forclusterlist)
    print(result)

结果：

[['类0'], ['类1', '类1', '类1', '类1', '类1'], ['类2', '类2', '类2', '类2', '类2', '类2'], ['类3'], ['噪声', '噪声', '噪声']]