On 04/16/2018 04:07 PM, Sidak Pal Singh wrote:
Hi everyone,
I was using scikit-learn KMeans algorithm to cluster pretrained
word-vectors. There are a few things which I found to be surprising
and wanted to get some feedback on.
- Based upon the 'labels_' assigned to each word-vector (i.e. cluster
memberships), I compute every cluster centroid as the average of the
word-vectors (corresponding to that cluster). Surprisingly, this seems
to be pretty different from the 'cluster_centers_'. Is there anything
that I am missing here?
If the algorithm did not fully converge, you just did one more step, so
the results are expected to be different.
- I was later using the verbose option to see if the clustering has
converged or not. I saw on the console log messages such as /"//center
shift 7.994126e-04 within tolerance 1.243425e-06"/. It seems that this
corresponds to some code in *kmeans_elkan.pyx*
(https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/_k_means_elkan.pyx).
- Lastly, another thing that seems strange is that I hadn't set the
tolerance value. So the default of 1e-4 should have been used. But if
you look again at the above log, it says /within tolerance
1.243425e-06 instead of 1e-4.
/
/https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/k_means_.py#L159
The tolerance is scaled by the variance of the data to be independent of
the scal/e
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
