{"action":"created","issue":{"url":"https://api.github.com/repos/pavanramkumar/pyglmnet/issues/122","repository_url":"https://api.github.com/repos/pavanramkumar/pyglmnet","labels_url":"https://api.github.com/repos/pavanramkumar/pyglmnet/issues/122/labels{/name}","comments_url":"https://api.github.com/repos/pavanramkumar/pyglmnet/issues/122/comments","events_url":"https://api.github.com/repos/pavanramkumar/pyglmnet/issues/122/events","html_url":"https://github.com/pavanramkumar/pyglmnet/pull/122","id":157280344,"number":122,"title":"[DOC] pyglmnet application and comparison against sklearn & refactoring of the developer documentation","user":{"login":"marquesVF","id":8534191,"avatar_url":"https://avatars.githubusercontent.com/u/8534191?v=3","gravatar_id":"","url":"https://api.github.com/users/marquesVF","html_url":"https://github.com/marquesVF","followers_url":"https://api.github.com/users/marquesVF/followers","following_url":"https://api.github.com/users/marquesVF/following{/other_user}","gists_url":"https://api.github.com/users/marquesVF/gists{/gist_id}","starred_url":"https://api.github.com/users/marquesVF/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/marquesVF/subscriptions","organizations_url":"https://api.github.com/users/marquesVF/orgs","repos_url":"https://api.github.com/users/marquesVF/repos","events_url":"https://api.github.com/users/marquesVF/events{/privacy}","received_events_url":"https://api.github.com/users/marquesVF/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":15,"created_at":"2016-05-27T19:58:53Z","updated_at":"2016-06-08T20:48:58Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/pavanramkumar/pyglmnet/pulls/122","html_url":"https://github.com/pavanramkumar/pyglmnet/pull/122","diff_url":"https://github.com/pavanramkumar/pyglmnet/pull/122.diff","patch_url":"https://github.com/pavanramkumar/pyglmnet/pull/122.patch"},"body":"I've been working on this application example using a real dataset to make a comparison between pyglmnet and sklearn. I think it should be reviewed for correctness and maybe it's incomplete and/or need more textual explanations. Anyway, I think this file can be used a start point.\r\n\r\nThe comparison is made with two approaches: r square and mean squared error (both implementations offered by sklearn). And the result are:\r\n\r\n> r^2 on test data using pyglmnet : 0.673593\r\n> mean square error on test data using pyglmnet : 0.010287\r\n> r^2 on test data using sklearn : 0.727229\r\n> mean square error on test data using sklearn : 0.010266\r\n\r\nThe plot also gives useful insights."},"comment":{"url":"https://api.github.com/repos/pavanramkumar/pyglmnet/issues/comments/224722786","html_url":"https://github.com/pavanramkumar/pyglmnet/pull/122#issuecomment-224722786","issue_url":"https://api.github.com/repos/pavanramkumar/pyglmnet/issues/122","id":224722786,"user":{"login":"pavanramkumar","id":3664715,"avatar_url":"https://avatars.githubusercontent.com/u/3664715?v=3","gravatar_id":"","url":"https://api.github.com/users/pavanramkumar","html_url":"https://github.com/pavanramkumar","followers_url":"https://api.github.com/users/pavanramkumar/followers","following_url":"https://api.github.com/users/pavanramkumar/following{/other_user}","gists_url":"https://api.github.com/users/pavanramkumar/gists{/gist_id}","starred_url":"https://api.github.com/users/pavanramkumar/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pavanramkumar/subscriptions","organizations_url":"https://api.github.com/users/pavanramkumar/orgs","repos_url":"https://api.github.com/users/pavanramkumar/repos","events_url":"https://api.github.com/users/pavanramkumar/events{/privacy}","received_events_url":"https://api.github.com/users/pavanramkumar/received_events","type":"User","site_admin":false},"created_at":"2016-06-08T20:48:58Z","updated_at":"2016-06-08T20:48:58Z","body":"@marquesVF Sorry i'm so late the party. \r\n\r\nFirst of all, good choice of dataset and great work! A few comments:\r\n\r\nThis works as both an application to real data and as a benchmark, so I'm happy to have it in the benchmarks folder for now.\r\n\r\nIt's important to note that yglmnet is somewhere between `glmnet` in `R` and `scikit-learn`, in that `scikit-learn`'s elastic net only offers linear models for now.\r\n\r\nThis leads me to an important fix:\r\n- The problem with using `distr=poissonexp` is that although the target variable can be interpreted as a count, it doesn't allow direct comparison to `scikit-learn`. So, I would recommend changing to `distr=normal` and seeing what results you get.\r\n\r\nAs a separate benchmark, I can work on comparing `distr=poisson` or `distr=poissonexp` with the same dataset."}}
{"actor":{"display_login":"pavanramkumar"}}