{"action":"created","issue":{"url":"https://api.github.com/repos/markovmodel/PyEMMA/issues/716","repository_url":"https://api.github.com/repos/markovmodel/PyEMMA","labels_url":"https://api.github.com/repos/markovmodel/PyEMMA/issues/716/labels{/name}","comments_url":"https://api.github.com/repos/markovmodel/PyEMMA/issues/716/comments","events_url":"https://api.github.com/repos/markovmodel/PyEMMA/issues/716/events","html_url":"https://github.com/markovmodel/PyEMMA/issues/716","id":138450163,"number":716,"title":"hmm.cktest fails","user":{"login":"fabian-paul","id":8878525,"avatar_url":"https://avatars.githubusercontent.com/u/8878525?v=3","gravatar_id":"","url":"https://api.github.com/users/fabian-paul","html_url":"https://github.com/fabian-paul","followers_url":"https://api.github.com/users/fabian-paul/followers","following_url":"https://api.github.com/users/fabian-paul/following{/other_user}","gists_url":"https://api.github.com/users/fabian-paul/gists{/gist_id}","starred_url":"https://api.github.com/users/fabian-paul/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/fabian-paul/subscriptions","organizations_url":"https://api.github.com/users/fabian-paul/orgs","repos_url":"https://api.github.com/users/fabian-paul/repos","events_url":"https://api.github.com/users/fabian-paul/events{/privacy}","received_events_url":"https://api.github.com/users/fabian-paul/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":5,"created_at":"2016-03-04T11:23:51Z","updated_at":"2016-03-10T17:38:20Z","closed_at":null,"body":"```\r\nHMM  <class 'pyemma.msm.estimators.maximum_likelihood_hmsm.MaximumLikelihoodHMSM'>\r\n3\r\nTraceback (most recent call last):\r\n  File \"scripts/msm_feature_coarse_grain.py\", line 162, in <module>\r\n    hmm_ck = hmm.cktest(mlags=10,conf=0.95,err_est=False,show_progress=True)\r\n  File \".../miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/msm/estimators/maximum_likelihood_hmsm.py\",\r\nline 317, in cktest\r\n    ck.estimate(self._dtrajs_full)\r\n  File \"./miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/_base/estimator.py\",\r\nline 342, in estimate\r\n    self._model = self._estimate(X)\r\n  File \"./miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/msm/estimators/lagged_model_validators.py\",\r\nline 137, in _estimate\r\n    n_jobs=self.n_jobs)\r\n  File \"./miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/_base/estimator.py\",\r\nline 300, in estimate_param_scan\r\n    res = pool(task_iter)\r\n  File \"./miniconda/lib/python2.7/site-packages/joblib/parallel.py\",\r\nline 653, in __call__\r\n    self.dispatch(function, args, kwargs)\r\n  File \"./miniconda/lib/python2.7/site-packages/joblib/parallel.py\",\r\nline 400, in dispatch\r\n    job = ImmediateApply(func, args, kwargs)\r\n  File \"./miniconda/lib/python2.7/site-packages/joblib/parallel.py\",\r\nline 138, in __init__\r\n    self.results = func(*args, **kwargs)\r\n  File \"./miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/_base/estimator.py\",\r\nline 132, in _estimate_param_scan_worker\r\n    model = estimator.estimate(X, **params)\r\n  File \"./miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/_base/estimator.py\",\r\nline 342, in estimate\r\n    self._model = self._estimate(X)\r\n  File \"./miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/msm/estimators/maximum_likelihood_hmsm.py\",\r\nline 189, in _estimate\r\n    assert _types.is_int(self.nstates) and self.nstates > 1 and\r\nself.nstates <= msm_init.nstates, \\\r\n  File \"./miniconda/lib/python2.7/site-packages/pyEMMA-2.0.3-py2.7-linux-x86_64.egg/pyemma/msm/models/msm.py\",\r\nline 202, in nstates\r\n    return self._nstates\r\nAttributeError: 'MaximumLikelihoodMSM' object has no attribute '_nstates'\r\n```"},"comment":{"url":"https://api.github.com/repos/markovmodel/PyEMMA/issues/comments/194970916","html_url":"https://github.com/markovmodel/PyEMMA/issues/716#issuecomment-194970916","issue_url":"https://api.github.com/repos/markovmodel/PyEMMA/issues/716","id":194970916,"user":{"login":"marscher","id":170287,"avatar_url":"https://avatars.githubusercontent.com/u/170287?v=3","gravatar_id":"","url":"https://api.github.com/users/marscher","html_url":"https://github.com/marscher","followers_url":"https://api.github.com/users/marscher/followers","following_url":"https://api.github.com/users/marscher/following{/other_user}","gists_url":"https://api.github.com/users/marscher/gists{/gist_id}","starred_url":"https://api.github.com/users/marscher/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/marscher/subscriptions","organizations_url":"https://api.github.com/users/marscher/orgs","repos_url":"https://api.github.com/users/marscher/repos","events_url":"https://api.github.com/users/marscher/events{/privacy}","received_events_url":"https://api.github.com/users/marscher/received_events","type":"User","site_admin":false},"created_at":"2016-03-10T17:38:20Z","updated_at":"2016-03-10T17:38:20Z","body":"I fear it won't solve the problem:\n\n```\nmarscher@area51:~/workspace/pyemma/doc$ conda install pyemma=2.0.3\nUsing Anaconda Cloud api site https://api.anaconda.org\nFetching package metadata: ........\nSolving package specifications: ...........\n\nPackage plan for installation in environment /home/marscher/miniconda:\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    numexpr-2.4.6              |      np110py34_0         334 KB\n    scikit-learn-0.17.1        |      np110py34_0         8.8 MB\n    pyemma-2.0.3               |           py34_0         692 KB\n    ------------------------------------------------------------\n                                           Total:         9.8 MB\n\nThe following packages will be UPDATED:\n\n    numexpr:      2.4.4-np110py34_p0 [mkl] --> 2.4.6-np110py34_0\n    scikit-learn: 0.17-np110py34_p1  [mkl] --> 0.17.1-np110py34_0\n\nThe following packages will be DOWNGRADED:\n\n    bhmm:         0.6.1-np110py34_0  --> 0.5.2-np110py34_0\n    pyemma:       2.0.4-py34_1       --> 2.0.3-py34_0\n\nProceed ([y]/n)? y\n\nFetching packages ...\nnumexpr-2.4.6- 100%\n|######################################################################################################################|\nTime: 0:00:00 429.82 kB/s\nscikit-learn-0 100%\n|######################################################################################################################|\nTime: 0:00:11 805.95 kB/s\npyemma-2.0.3-p 100%\n|######################################################################################################################|\nTime: 0:00:01 653.47 kB/s\nExtracting packages ...\n[      COMPLETE\n]|#########################################################################################################################################|\n100%\nUnlinking packages ...\n[      COMPLETE\n]|#########################################################################################################################################|\n100%\nLinking packages ...\n[      COMPLETE\n]|#########################################################################################################################################|\n100%\nmarscher@area51:~/workspace/pyemma/doc$ conda install pyemma=2.0.4\nUsing Anaconda Cloud api site https://api.anaconda.org\nFetching package metadata: ........\nSolving package specifications: ...........\n\nPackage plan for installation in environment /home/marscher/miniconda:\n\nThe following packages will be UPDATED:\n\n    pyemma: 2.0.3-py34_0 --> 2.0.4-py34_1\n\nProceed ([y]/n)? y\n\nUnlinking packages ...\n[      COMPLETE\n]|#########################################################################################################################################|\n100%\nLinking packages ...\n[      COMPLETE\n]|#########################################################################################################################################|\n100%\n\n```\n"}}