{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn-contrib/polylearn/issues/1","repository_url":"https://api.github.com/repos/scikit-learn-contrib/polylearn","labels_url":"https://api.github.com/repos/scikit-learn-contrib/polylearn/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn-contrib/polylearn/issues/1/comments","events_url":"https://api.github.com/repos/scikit-learn-contrib/polylearn/issues/1/events","html_url":"https://github.com/scikit-learn-contrib/polylearn/pull/1","id":166148534,"number":1,"title":"Speedup","user":{"login":"vene","id":241745,"avatar_url":"https://avatars.githubusercontent.com/u/241745?v=3","gravatar_id":"","url":"https://api.github.com/users/vene","html_url":"https://github.com/vene","followers_url":"https://api.github.com/users/vene/followers","following_url":"https://api.github.com/users/vene/following{/other_user}","gists_url":"https://api.github.com/users/vene/gists{/gist_id}","starred_url":"https://api.github.com/users/vene/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/vene/subscriptions","organizations_url":"https://api.github.com/users/vene/orgs","repos_url":"https://api.github.com/users/vene/repos","events_url":"https://api.github.com/users/vene/events{/privacy}","received_events_url":"https://api.github.com/users/vene/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":23,"created_at":"2016-07-18T17:32:12Z","updated_at":"2016-08-01T16:32:37Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/scikit-learn-contrib/polylearn/pulls/1","html_url":"https://github.com/scikit-learn-contrib/polylearn/pull/1","diff_url":"https://github.com/scikit-learn-contrib/polylearn/pull/1.diff","patch_url":"https://github.com/scikit-learn-contrib/polylearn/pull/1.patch"},"body":"Switch everything to typed memoryviews and inline direct solver update.\r\n\r\n```\r\n20 newsgroups\r\n=============\r\nX_train.shape = (11314, 130107)\r\nX_train.format = csc\r\nX_train.dtype = float64\r\nX_train density = 0.001214353154362896\r\ny_train (11314,)\r\nX_test (7532, 130107)\r\nX_test.format = csc\r\nX_test.dtype = float64\r\ny_test (7532,)\r\n\r\nClassifier Training\r\n===================\r\n/home/vlad/code/polylearn/polylearn/factorization_machine.py:118: UserWarning: Objective did not converge. Increase max_iter.\r\n  warnings.warn(\"Objective did not converge. Increase max_iter.\")\r\nTraining fm-2 ... done\r\nTraining fm-3 ... done\r\n/home/vlad/code/polylearn/polylearn/polynomial_network.py:99: UserWarning: Objective did not converge. Increase max_iter.\r\n  warnings.warn(\"Objective did not converge. Increase max_iter.\")\r\nTraining polynet-2 ... done\r\nTraining polynet-3 ... done\r\nClassification performance:\r\n===========================\r\n\r\nClassifier            train       test         f1   accuracy\r\n------------------------------------------------------------\r\nfm-3               36.3608s    0.3642s     0.4009     0.9663\r\nfm-2               31.7401s    0.1225s     0.6126     0.9740\r\npolynet-3           6.6819s    0.0685s     0.6992     0.9796\r\npolynet-2           5.9581s    0.0693s     0.7310     0.9807\r\n\r\n>>> on branch speedup\r\n\r\nClassifier            train       test         f1   accuracy\r\n------------------------------------------------------------\r\nfm-3               11.7684s    0.3643s     0.4009     0.9663\r\nfm-2                7.2895s    0.1197s     0.6126     0.9740\r\npolynet-3           6.6159s    0.0685s     0.6992     0.9796\r\npolynet-2           5.9382s    0.0683s     0.7310     0.9807\r\n\r\n\r\n```"},"comment":{"url":"https://api.github.com/repos/scikit-learn-contrib/polylearn/issues/comments/236633564","html_url":"https://github.com/scikit-learn-contrib/polylearn/pull/1#issuecomment-236633564","issue_url":"https://api.github.com/repos/scikit-learn-contrib/polylearn/issues/1","id":236633564,"user":{"login":"vene","id":241745,"avatar_url":"https://avatars.githubusercontent.com/u/241745?v=3","gravatar_id":"","url":"https://api.github.com/users/vene","html_url":"https://github.com/vene","followers_url":"https://api.github.com/users/vene/followers","following_url":"https://api.github.com/users/vene/following{/other_user}","gists_url":"https://api.github.com/users/vene/gists{/gist_id}","starred_url":"https://api.github.com/users/vene/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/vene/subscriptions","organizations_url":"https://api.github.com/users/vene/orgs","repos_url":"https://api.github.com/users/vene/repos","events_url":"https://api.github.com/users/vene/events{/privacy}","received_events_url":"https://api.github.com/users/vene/received_events","type":"User","site_admin":false},"created_at":"2016-08-01T16:32:37Z","updated_at":"2016-08-01T16:32:37Z","body":"I fixed the windows bug! It was my fault, of course. I forgot to initialize a `double` scalar to 0, and it turns out this doesn't behave well on MSVC (oddly, only for py2, not for py3: must be because of the different compiler version?!)\r\n\r\nAppveyor is green now for both win32 and win64. (I canceled a duplicate build, triggered by the fact that this branch is on scikit-learn-contrib rather than on my fork, feel free to ignore that.)\r\n\r\nI'll remove the ugly `_sq` thing, update the benchmarks, then ping again for a final review before merging.\r\n"}}
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