franknoe
commented
8 years ago Ouch. That looks like being due to a recent change in the Estimator class, but this function should be definitely run in one of the tests, so I don't see how we could have overlooked this. We may need a patch.
I'll have a look. Did this work with 2.0.4?
Am 08/04/16 um 18:09 schrieb Guillermo Pérez-Hernández:
pyemma.version '2.1-9-g4936d0d' This happened:
its[pp]= pyemma.msm.timescales_hmsm([dtraj[::rs]for dtrajin cl[pp].dtrajs],4, lags=lags_fr[rs].astype(int),nits=5)|AttributeError Traceback (most recent call last) /home/mi/gph82/programs/PyEmma/pyemma/_base/estimator.py in model(self) 371 try: --> 372 return self._model 373 except AttributeError: AttributeError: 'MaximumLikelihoodHMSM' object has no attribute '_model' During handling of the above exception, another exception occurred: AttributeError Traceback (most recent call last)
in () 3 for pp in prot: 4 pp = pp[:2] ----> 5 its[pp] = pyemma.msm.timescales_hmsm([dtraj[::rs] for dtraj in cl[pp].dtrajs], 4, lags=lags_fr[rs].astype(int), nits=5) 6 7 /home/mi/gph82/programs/PyEmma/pyemma/msm/api.py in timescales_hmsm(dtrajs, nstates, lags, nits, reversible, stationary, connectivity, mincount_connectivity, separate, errors, nsamples, stride, n_jobs, show_progress) 848 itsobj = _ImpliedTimescales(estimator, lags=lags, nits=nits, n_jobs=n_jobs, 849 show_progress=show_progress) --> 850 itsobj.estimate(dtrajs) 851 return itsobj 852 /home/mi/gph82/programs/PyEmma/pyemma/_base/estimator.py in estimate(self, X, **params) 341 if params: 342 self.set_params(**params) --> 343 self._model = self._estimate(X) 344 self._estimated = True 345 return self /home/mi/gph82/programs/PyEmma/pyemma/msm/estimators/implied_timescales.py in _estimate(self, data) 146 self._models, self._estimators = estimate_param_scan(self.estimator, data, param_sets, failfast=False, 147 return_estimators=True, n_jobs=self.n_jobs, --> 148 progress_reporter=self) 149 150 ### PROCESS RESULTS /home/mi/gph82/programs/PyEmma/pyemma/_base/estimator.py in estimate_param_scan(estimator, X, param_sets, evaluate, evaluate_args, failfast, return_estimators, n_jobs, progress_reporter) 296 297 # container for model or function evaluations --> 298 res = pool(task_iter) 299 300 if progress_reporter is not None: /home/mi/gph82/miniconda3/lib/python3.4/site-packages/joblib-0.9.4-py3.4.egg/joblib/parallel.py in __call__(self, iterable) 798 # was dispatched. In particular this covers the edge 799 # case of Parallel used with an exhausted iterator. --> 800 while self.dispatch_one_batch(iterator): 801 self._iterating = True 802 else: /home/mi/gph82/miniconda3/lib/python3.4/site-packages/joblib-0.9.4-py3.4.egg/joblib/parallel.py in dispatch_one_batch(self, iterator) 656 return False 657 else: --> 658 self._dispatch(tasks) 659 return True 660 /home/mi/gph82/miniconda3/lib/python3.4/site-packages/joblib-0.9.4-py3.4.egg/joblib/parallel.py in _dispatch(self, batch) 564 565 if self._pool is None: --> 566 job = ImmediateComputeBatch(batch) 567 self._jobs.append(job) 568 self.n_dispatched_batches += 1 /home/mi/gph82/miniconda3/lib/python3.4/site-packages/joblib-0.9.4-py3.4.egg/joblib/parallel.py in __init__(self, batch) 178 # Don't delay the application, to avoid keeping the input 179 # arguments in memory --> 180 self.results = batch() 181 182 def get(self): /home/mi/gph82/miniconda3/lib/python3.4/site-packages/joblib-0.9.4-py3.4.egg/joblib/parallel.py in **call**(self) 70 71 def **call**(self): ---> 72 return [func(_args, *_kwargs) for func, args, kwargs in self.items] 73 74 def **len**(self): /home/mi/gph82/miniconda3/lib/python3.4/site-packages/joblib-0.9.4-py3.4.egg/joblib/parallel.py in (.0) 70 71 def **call**(self): ---> 72 return [func(_args, *_kwargs) for func, args, kwargs in self.items] 73 74 def **len**(self): /home/mi/gph82/programs/PyEmma/pyemma/_base/estimator.py in _estimate_param_scan_worker(estimator, params, X, evaluate, evaluate_args, failfast) 142 # deal with result 143 if evaluate is None: # we want full models --> 144 res.append(estimator.model) 145 # we want to evaluate function(s) of the model 146 elif _types.is_iterable(evaluate): /home/mi/gph82/programs/PyEmma/pyemma/_base/estimator.py in model(self) 373 except AttributeError: 374 raise AttributeError( --> 375 'Model has not yet been estimated. Call estimate(X) or fit(X) first') AttributeError: Model has not yet been estimated. Call estimate(X) or fit(X) first | — You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub https://github.com/markovmodel/PyEMMA/issues/764
Prof. Dr. Frank Noe Head of Computational Molecular Biology group Freie Universitaet Berlin
Phone: (+49) (0)30 838 75354 Web: research.franknoe.de
gph82
stefdoerr
marscher
pyemma.version '2.1-9-g4936d0d' This happened: