{"action":"opened","issue":{"url":"https://api.github.com/repos/catboost/catboost/issues/244","repository_url":"https://api.github.com/repos/catboost/catboost","labels_url":"https://api.github.com/repos/catboost/catboost/issues/244/labels{/name}","comments_url":"https://api.github.com/repos/catboost/catboost/issues/244/comments","events_url":"https://api.github.com/repos/catboost/catboost/issues/244/events","html_url":"https://github.com/catboost/catboost/issues/244","id":295626428,"number":244,"title":"prediction throws CarboostError","user":{"login":"myusernameisunique","id":36269418,"avatar_url":"https://avatars0.githubusercontent.com/u/36269418?v=4","gravatar_id":"","url":"https://api.github.com/users/myusernameisunique","html_url":"https://github.com/myusernameisunique","followers_url":"https://api.github.com/users/myusernameisunique/followers","following_url":"https://api.github.com/users/myusernameisunique/following{/other_user}","gists_url":"https://api.github.com/users/myusernameisunique/gists{/gist_id}","starred_url":"https://api.github.com/users/myusernameisunique/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/myusernameisunique/subscriptions","organizations_url":"https://api.github.com/users/myusernameisunique/orgs","repos_url":"https://api.github.com/users/myusernameisunique/repos","events_url":"https://api.github.com/users/myusernameisunique/events{/privacy}","received_events_url":"https://api.github.com/users/myusernameisunique/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-02-08T18:54:44Z","updated_at":"2018-02-08T18:54:44Z","closed_at":null,"author_association":"NONE","body":"CatboostError: catboost/libs/algo/apply.cpp:18: Insufficient float features count\r\n\r\nAll my features are categorical.\r\n\r\nIt trained just fine. I was actually surprised by how well my evaluation set's AUC was improving while it was training.\r\n\r\nI declared all pools with correct categorical indices.\r\n\r\nMy process:\r\n...\r\ndata preparation\r\nconvert columns to pandas categorical columns\r\n...\r\nmodel = catboost.CatBoostClassifier(...)\r\nlearn = catboost.Pool(...)\r\neval = catboost.Pool(...)\r\nmodel.fit(learn, ..., eval_set=eval, ...)\r\nmodel.save_model(...)\r\n...\r\nmodel = catboost.CatBoostClassifier()\r\nmodel.load_model(...)\r\npredict = catboost.Pool(...)\r\nmodel.predict(predict, prediction_type='Probability')\r\n\r\nthen ..........\r\npredictions = np.array(self._base_predict(data, prediction_type, ntree_start, ntree_end, thread_count, verbose))\r\n  File \"_catboost.pyx\", line 1049, in _catboost._CatBoostBase._base_predict\r\n  File \"_catboost.pyx\", line 858, in _catboost._CatBoost._base_predict\r\n  File \"_catboost.pyx\", line 861, in _catboost._CatBoost._base_predict\r\n_catboost.CatboostError: catboost/libs/algo/apply.cpp:18: Insufficient float features count 0<517\r\n\r\n\r\nAny idea?"}}
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