2012/1/16 Lars Buitinck <[email protected]>: > 2012/1/16 Mathieu Blondel <[email protected]>: >> I wrote a class which takes a base estimator in its constructor. For >> efficiency reasons, it is best if the estimator supports dense input. >> I would like thus to issue a warning if the given estimator supports >> only sparse input (as is the case of e.g. svm.sparse.LinearSVC). This >> raises the question of how shall we make this information available in >> the scikit. One solution would be to use class variables in all >> estimators. For example: > > I think this problem would be solved if we merged the sparse and dense > estimators, like you've been doing with SGD. I'd very much prefer that > solution.
+1 for going on with the merge of ndarray / sparse matrix implementations. However that won't solve the fact that some estimators are array only implementations and it could be would be very for client code e.g. nltk to be able to instrospect if estimators instances and classes have special capabilities, esp. in terms of accepted input representations. I agree that we should take care of not drowning the scikit-learn code base into frameworkish boiler plate. We could still make those annotation optional and assume that if no annotation is given then only arrays are accepted as input. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ RSA(R) Conference 2012 Mar 27 - Feb 2 Save $400 by Jan. 27 Register now! http://p.sf.net/sfu/rsa-sfdev2dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
