On 05/25/2012 09:02 AM, Gael Varoquaux wrote: > Hi list, > > A lot of clustering algorithms can be initiated randomely and thus on the > same data give different results because of the non-convexity of the > criterion. > > One trivial source of non-reproducibility is the fact that labels can be > permuted: even if the algorithm find the same clusters, it may give > different labels to these. This renders testing and exploration harder, > but its easy to fix. > > Indeed, if we use as a convention that as we consider training samples in > the ordering in which they are given, cluster labels are found in an > ordered way, all we need to do is to add the following line at the end of > the fit: > > labels = np.unique(labels, return_index=True)[1][labels] -0 Why not, but this is easy and safe to do only in some cases: -- do not forget to permute all the label-related info (cluster centers, weights, covariance)... -- In case of hierarchical clustering, you need to decide whether you break the consistency of the labelling across level of the hierarchy. > > provided that the labels id are not used elsewhere, of course. Yes, exactly. > I'd like to do this for kmeans, and maybe a few other algorithms where it > is really easy to do. What do people think? > > Gael > > ------------------------------------------------------------------------------ > Live Security Virtual Conference > Exclusive live event will cover all the ways today's security and > threat landscape has changed and how IT managers can respond. Discussions > will include endpoint security, mobile security and the latest in malware > threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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