clustering-metrics | Python implementations of various clustering metrics | Analytics library

I have been using Sklearn's Kmeans implementation

I have been clustering a dataset which is labeled, and I have been using sklearn's clustering metrics in order to test the clustering performance.

Sklearn's Kmeans clustering output is as you know a list of numbers in the range of k_clusters. However my labels are strings.

So far I had no problems with them since the metrics from sklearn.metrics.cluster work with mixed inputs (int & str label lists).

However now I want to use some of the classification metrics and from what I gather, the inputs k_true and k_pred need to be of the same set. Either numbers in range of k, or then string labels that my dataset is using. If I try it, it returns the following error:

AttributeError: 'bool' object has no attribute 'sum'

So, how could I translate the k_means labels into an other type of labels? Or even the other way around (string labels -> integer labels).

How could I even begin implementing it? Since k_means is pretty non-deterministic, the labels might change from iteration to iteration. Is there a legit way in order to correctly translate Kmeans labels?

EDIT:

EXAMPLE

for k = 4

kmeans output: [0,3,3,2,........0]

class labels : ['CAT','DOG','DOG','BIRD',.......'CHICKEN']