Question

我在这里搜索并用Google搜索，但无济于事。当在Weka中进行聚类时，有一个方便的选项，类到聚类，它匹配算法产生的聚类，例如简单的k-means，你提供的'ground truth'类标签作为class属性。这样我们就可以看到集群的准确性（％不正确）。

现在，我如何在Matlab中实现这一点，即翻译我的clusterClasses向量，例如[1, 1, 2, 1, 3, 2, 3, 1, 1, 1]与提供的地面实况标签向量相同的索引，例如[2, 2, 2, 3, 1, 3]？

我认为它可能基于集群中心和标签中心，但我不确定如何实施！

非常感谢任何帮助。

文森特

Answer 1

几个月前，我在做集群时偶然发现了类似的问题。我没有长时间搜索内置解决方案（虽然我确信它们必须存在）并最终编写了我自己的小脚本，以便将我发现的标签与最基本的事实相匹配。代码很粗糙，但它应该让你开始。

它基于尝试所有可能的标签重新排列，以确定最适合真实向量的女巫。这意味着，如果群集结果yte = [3 3 2 1]具有基本事实y = [1 1 2 3]，则脚本会尝试将[3 3 2 1], [3 3 1 2], [2 2 3 1], [2 2 1 3], [1 1 2 3] and [1 1 3 2]与y匹配，以找到最佳匹配。

这是基于使用内置脚本perms()，无法处理10个以上的唯一群集。对于7-10个独特的集群，代码也可能会变慢，因为复杂性会逐渐增加。

function [accuracy, true_labels, CM] = calculateAccuracy(yte, y)
%# Function for calculating clustering accuray and matching found 
%# labels with true labels. Assumes yte and y both are Nx1 vectors with
%# clustering labels. Does not support fuzzy clustering.
%#
%# Algorithm is based on trying out all reorderings of cluster labels, 
%# e.g. if yte = [1 2 2], try [1 2 2] and [2 1 1] so see witch fit 
%# the truth vector the best. Since this approach makes use of perms(),
%# the code will not run for unique(yte) greater than 10, and it will slow
%# down significantly for number of clusters greater than 7.
%#
%# Input:
%#   yte - result from clustering (y-test)
%#   y   - truth vector
%#
%# Output:
%#   accuracy    -   Overall accuracy for entire clustering (OA). For
%#                   overall error, use OE = 1 - OA.
%#   true_labels -   Vector giving the label rearangement witch best 
%#                   match the truth vector (y).
%#   CM          -   Confusion matrix. If unique(yte) = 4, produce a
%#                   4x4 matrix of the number of different errors and  
%#                   correct clusterings done.

N = length(y);

cluster_names = unique(yte);
accuracy = 0;
maxInd = 1;

perm = perms(unique(y));
[pN pM] = size(perm);

true_labels = y;

for i=1:pN
    flipped_labels = zeros(1,N);
    for cl = 1 : pM
        flipped_labels(yte==cluster_names(cl)) = perm(i,cl);
    end

    testAcc = sum(flipped_labels == y')/N;
    if testAcc > accuracy
        accuracy = testAcc;
        maxInd = i;
        true_labels = flipped_labels;
    end

end

CM = zeros(pM,pM);
for rc = 1 : pM
    for cc = 1 : pM
        CM(rc,cc) = sum( ((y'==rc) .* (true_labels==cc)) );
    end
end

示例：

[acc newLabels CM] = calculateAccuracy([3 2 2 1 2 3]',[1 2 2 3 3 3]')

acc =

0.6667


newLabels =

 1     2     2     3     2     1


CM =

 1     0     0
 0     2     0
 1     1     1

Answer 2

您可能希望研究更灵活的评估群集的方法。例如，对计数指标。

“class = cluster”假设对于从机器学习进入群集的人来说是典型的。但是你应该假设某些类可能包含多个集群，或者多个类实际上是集群。这些是应实际检测到的聚类算法的有趣情况。

Answer 3

对于Python，我需要这个确切的东西，并转换了Vidar发布的代码（可接受的答案）。我将代码分享给任何有兴趣的人。我重命名了变量并删除了混淆矩阵（无论如何，大多数用于机器学习的库都内置了函数）。我注意到由Vincent（http://www.mathworks.com/matlabcentral/fileexchange/32197-clustering-results-measurement）链接的更快的实现为时已晚。可能更好地将其改编成Python。

#tested with python 3.6
def remap_labels(pred_labels, true_labels):
    """Rename prediction labels (clustered output) to best match true labels."""
    # from itertools import permutations # import this into script.
    pred_labels, true_labels = np.array(pred_labels), np.array(true_labels)
    assert pred_labels.ndim == 1 == true_labels.ndim
    assert len(pred_labels) == len(true_labels)
    cluster_names = np.unique(pred_labels)
    accuracy = 0

    perms = np.array(list(permutations(np.unique(true_labels))))

    remapped_labels = true_labels
    for perm in perms:
        flipped_labels = np.zeros(len(true_labels))
        for label_index, label in enumerate(cluster_names):
            flipped_labels[pred_labels == label] = perm[label_index]

        testAcc = np.sum(flipped_labels == true_labels) / len(true_labels)
        if testAcc > accuracy:
            accuracy = testAcc
            remapped_labels = flipped_labels

    return accuracy, remapped_labels

如何在Matlab中将群集标签与我的“基础事实”标签进行匹配

3 个答案: