{"action":"closed","issue":{"url":"https://api.github.com/repos/biolab/orange3/issues/1719","repository_url":"https://api.github.com/repos/biolab/orange3","labels_url":"https://api.github.com/repos/biolab/orange3/issues/1719/labels{/name}","comments_url":"https://api.github.com/repos/biolab/orange3/issues/1719/comments","events_url":"https://api.github.com/repos/biolab/orange3/issues/1719/events","html_url":"https://github.com/biolab/orange3/issues/1719","id":187327982,"node_id":"MDU6SXNzdWUxODczMjc5ODI=","number":1719,"title":"Sparse data may cause problems for some preprocessors","user":{"login":"BlazZupan","id":726604,"node_id":"MDQ6VXNlcjcyNjYwNA==","avatar_url":"https://avatars1.githubusercontent.com/u/726604?v=4","gravatar_id":"","url":"https://api.github.com/users/BlazZupan","html_url":"https://github.com/BlazZupan","followers_url":"https://api.github.com/users/BlazZupan/followers","following_url":"https://api.github.com/users/BlazZupan/following{/other_user}","gists_url":"https://api.github.com/users/BlazZupan/gists{/gist_id}","starred_url":"https://api.github.com/users/BlazZupan/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/BlazZupan/subscriptions","organizations_url":"https://api.github.com/users/BlazZupan/orgs","repos_url":"https://api.github.com/users/BlazZupan/repos","events_url":"https://api.github.com/users/BlazZupan/events{/privacy}","received_events_url":"https://api.github.com/users/BlazZupan/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2016-11-04T13:03:37Z","updated_at":"2018-07-27T07:59:55Z","closed_at":"2018-07-27T07:59:55Z","author_association":"CONTRIBUTOR","body":"<!--\r\nThis is an issue template. Please fill in the relevant details in the\r\nsections below.\r\n-->\r\n\r\n##### Orange version\r\n3.3.8 on Windows 7\r\n\r\n##### Expected behavior\r\nOrange should seamlessly propagate sparse data through the pipeline.\r\n\r\n##### Actual behavior\r\nSome preprocessors transform sparse data to dense data and hence clog memory, Orange crashes with MemoryError. An obvious example of such preprocessor is imputation, and is invoked before (any?) learner of scikit. \r\n\r\nA typical trace of the error is:\r\n\r\n```\r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\canvas\\\\scheme\\\\widgetsscheme.py\\ line 823, in process_signals_for_widget \r\nhandler(*args) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\widgets\\\\utils\\\\sql.py\\ line 28, in new_f \r\nreturn f(widget, data, *args, **kwargs) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\widgets\\\\utils\\\\owlearnerwidget.py\\ line 159, in set_data \r\nself.update_model() \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\widgets\\\\utils\\\\owlearnerwidget.py\\ line 175, in update_model \r\nself.model = self.learner(self.data) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\base.py\\ line 249, in __call__ \r\nm = super().__call__(data) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\base.py\\ line 44, in __call__ \r\ndata = self.preprocess(data) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\base.py\\ line 239, in preprocess \r\ndata = super().preprocess(data) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\base.py\\ line 68, in preprocess \r\ndata = pp(data) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\Orange\\\\preprocess\\\\preprocess.py\\ line 168, in __call__ \r\nX = self.imputer.fit_transform(data.X) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\sklearn\\\\base.py\\ line 494, in fit_transform \r\nreturn self.fit(X, **fit_params).transform(X) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\sklearn\\\\preprocessing\\\\imputation.py\\ line 313, in transform \r\nforce_all_finite=False, copy=self.copy) \r\n File \\\"C:\\\\Python34\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\validation.py\\ line 382, in check_array \r\narray = np.array(array, dtype=dtype, order=order, copy=copy) \r\nMemoryError\r\n```\r\n\r\n##### Steps to reproduce the behavior\r\nWe have spotted this type of error from bug reports and inferred the cause from error traces.\r\n"}}
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