{"action":"created","issue":{"url":"https://api.github.com/repos/mblondel/lightning/issues/40","labels_url":"https://api.github.com/repos/mblondel/lightning/issues/40/labels{/name}","comments_url":"https://api.github.com/repos/mblondel/lightning/issues/40/comments","events_url":"https://api.github.com/repos/mblondel/lightning/issues/40/events","html_url":"https://github.com/mblondel/lightning/pull/40","id":117170399,"number":40,"title":"[MRG] Just in time SAGA.","user":{"login":"zermelozf","id":971180,"avatar_url":"https://avatars.githubusercontent.com/u/971180?v=3","gravatar_id":"","url":"https://api.github.com/users/zermelozf","html_url":"https://github.com/zermelozf","followers_url":"https://api.github.com/users/zermelozf/followers","following_url":"https://api.github.com/users/zermelozf/following{/other_user}","gists_url":"https://api.github.com/users/zermelozf/gists{/gist_id}","starred_url":"https://api.github.com/users/zermelozf/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zermelozf/subscriptions","organizations_url":"https://api.github.com/users/zermelozf/orgs","repos_url":"https://api.github.com/users/zermelozf/repos","events_url":"https://api.github.com/users/zermelozf/events{/privacy}","received_events_url":"https://api.github.com/users/zermelozf/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":3,"created_at":"2015-11-16T17:03:14Z","updated_at":"2015-11-18T14:13:17Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/mblondel/lightning/pulls/40","html_url":"https://github.com/mblondel/lightning/pull/40","diff_url":"https://github.com/mblondel/lightning/pull/40.diff","patch_url":"https://github.com/mblondel/lightning/pull/40.patch"},"body":"A squashed version of #38 ontaining:\r\n- SAGA algorithm in cython.\r\n- Basic python version of SAG and SAGA for testing.\r\n- Support for proximity operators through the `Penalty` base class.\r\n- L1 proximity operator with just in time update for sparse data.\r\n"},"comment":{"url":"https://api.github.com/repos/mblondel/lightning/issues/comments/157724898","html_url":"https://github.com/mblondel/lightning/pull/40#issuecomment-157724898","issue_url":"https://api.github.com/repos/mblondel/lightning/issues/40","id":157724898,"user":{"login":"agramfort","id":161052,"avatar_url":"https://avatars.githubusercontent.com/u/161052?v=3","gravatar_id":"","url":"https://api.github.com/users/agramfort","html_url":"https://github.com/agramfort","followers_url":"https://api.github.com/users/agramfort/followers","following_url":"https://api.github.com/users/agramfort/following{/other_user}","gists_url":"https://api.github.com/users/agramfort/gists{/gist_id}","starred_url":"https://api.github.com/users/agramfort/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/agramfort/subscriptions","organizations_url":"https://api.github.com/users/agramfort/orgs","repos_url":"https://api.github.com/users/agramfort/repos","events_url":"https://api.github.com/users/agramfort/events{/privacy}","received_events_url":"https://api.github.com/users/agramfort/received_events","type":"User","site_admin":false},"created_at":"2015-11-18T14:13:17Z","updated_at":"2015-11-18T14:13:17Z","body":"ok for the benefit of SAGA.\n\nin terms of computation time what I read is that it's pretty much the same.\n\nthx\n\nOn Wed, Nov 18, 2015 at 2:59 PM, Arnaud Rachez <notifications@github.com>\nwrote:\n\n> @agramfort <https://github.com/agramfort> We have updated this gist\n> <https://gist.github.com/zermelozf/d4670d6cffea09f6e6f3> with sklearn's\n> LogisticRegression.\n> Multiclass (OVR) time (sec.) score SAGClassifier 8.727 0.1676\n> SAGAClassifier 12.245 0.1757 LogisticRegression 12.061 0.1750\n>\n> I have assumed that lightning does OVR for multi class problems (@mblondel\n> <https://github.com/mblondel> correct me if I misunderstood).\n>\n> The score discrepancy seems to be caused by the different stopping\n> criteria. We have not (yet) rigorously compared the convergence speed (to\n> the optimum) of SAG vs SAGA. The advantage of SAGA over SAG is the\n> possibility to specify an arbitrary proximity operator.\n>\n> In term of code speed, adding tol=0 leads to a runtime advantage for\n> sklearn for 100 iterations over the whole 20newsgroup dataset.\n> 100 iterations time (sec.) score SAGClassifier 198 0.174872665535\n> SAGAClassifier 215 0.174872665535 LogisticRegression 117 0.174872665535\n>\n> —\n> Reply to this email directly or view it on GitHub\n> <https://github.com/mblondel/lightning/pull/40#issuecomment-157720960>.\n>\n"}}