{"action":"closed","issue":{"url":"https://api.github.com/repos/jpmml/jpmml-sklearn/issues/2","labels_url":"https://api.github.com/repos/jpmml/jpmml-sklearn/issues/2/labels{/name}","comments_url":"https://api.github.com/repos/jpmml/jpmml-sklearn/issues/2/comments","events_url":"https://api.github.com/repos/jpmml/jpmml-sklearn/issues/2/events","html_url":"https://github.com/jpmml/jpmml-sklearn/issues/2","id":122103045,"number":2,"title":"Support for sparse matrix types","user":{"login":"vruusmann","id":3462679,"avatar_url":"https://avatars.githubusercontent.com/u/3462679?v=3","gravatar_id":"","url":"https://api.github.com/users/vruusmann","html_url":"https://github.com/vruusmann","followers_url":"https://api.github.com/users/vruusmann/followers","following_url":"https://api.github.com/users/vruusmann/following{/other_user}","gists_url":"https://api.github.com/users/vruusmann/gists{/gist_id}","starred_url":"https://api.github.com/users/vruusmann/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/vruusmann/subscriptions","organizations_url":"https://api.github.com/users/vruusmann/orgs","repos_url":"https://api.github.com/users/vruusmann/repos","events_url":"https://api.github.com/users/vruusmann/events{/privacy}","received_events_url":"https://api.github.com/users/vruusmann/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2015-12-14T18:35:59Z","updated_at":"2015-12-20T22:19:35Z","closed_at":"2015-12-20T22:19:35Z","body":"Selected SkLearn estimators can be fitted with sparse data. This is rather common when working with high-dimensional (n > 10'000) feature spaces.\r\n\r\nCurrently, such estimators can be handled by JPMML-SkLearn when they have been post-processed by converting attribute values from sparse matrix datatypes (eg. `scipy.sparse.csc`, `scipy.sparse.csr`) to `numpy.ndarray` datatype.\r\n\r\nIn the future, it would be desirable to perform such conversions in Java library code."}}