in notebook 07, this line :
hmm_4 = pyemma.msm.bayesian_hidden_markov_model(cluster.dtrajs, nstates=4, lag=1, dt_traj='1 ps', nsamples=50)
produces this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[5], line 1
----> 1 hmm_4 = pyemma.msm.bayesian_hidden_markov_model(cluster.dtrajs, nstates=4, lag=1, dt_traj='1 ps', nsamples=50)
File ~/miniconda3/envs/emma/lib/python3.11/site-packages/pyemma/msm/api.py:1375, in bayesian_hidden_markov_model(dtrajs, nstates, lag, nsamples, reversible, stationary, connectivity, mincount_connectivity, separate, observe_nonempty, stride, conf, dt_traj, store_hidden, show_progress)
1217 r""" Bayesian Hidden Markov model estimate using Gibbs sampling of the posterior
1218
1219 Returns a :class:`BayesianHMSM` that contains
(...)
1368
1369 """
1370 bhmsm_estimator = _Bayes_HMSM(lag=lag, nstates=nstates, stride=stride, nsamples=nsamples, reversible=reversible,
1371 stationary=stationary,
1372 connectivity=connectivity, mincount_connectivity=mincount_connectivity,
1373 separate=separate, observe_nonempty=observe_nonempty,
1374 dt_traj=dt_traj, conf=conf, store_hidden=store_hidden, show_progress=show_progress)
-> 1375 return bhmsm_estimator.estimate(dtrajs)
File ~/miniconda3/envs/emma/lib/python3.11/site-packages/pyemma/_base/estimator.py:418, in Estimator.estimate(self, X, **params)
416 if params:
417 self.set_params(**params)
--> 418 self._model = self._estimate(X)
419 # ensure _estimate returned something
420 assert self._model is not None
File ~/miniconda3/envs/emma/lib/python3.11/site-packages/pyemma/msm/estimators/bayesian_hmsm.py:313, in BayesianHMSM._estimate(self, dtrajs)
304 else:
305 estimator = BayesianHMM.default(dtrajs, n_hidden_states=self.nstates, lagtime=self.lag,
306 n_samples=self.nsamples, stride=self.stride,
307 initial_distribution_prior=self.p0_prior,
(...)
310 stationary=self.stationary,
311 prior_submodel=True, separate=self.separate)
--> 313 estimator.fit(dtrajs, n_burn_in=0, n_thin=1, progress=progress)
314 model = estimator.fetch_model()
315 if self.show_progress:
File ~/miniconda3/envs/emma/lib/python3.11/site-packages/deeptime/base.py:417, in _ImmutableInputData.__call__(self, *args, **kwargs)
415 # here we invoke the immutable setting context manager.
416 with self:
--> 417 return self.fit_method(*args, **kwargs)
File ~/miniconda3/envs/emma/lib/python3.11/site-packages/deeptime/markov/hmm/_bayesian_hmm.py:610, in BayesianHMM.fit(self, data, n_burn_in, n_thin, progress, **kwargs)
608 # Collect data.
609 models = []
--> 610 for _ in progress(range(self.n_samples), desc="Drawing samples", leave=False):
611 # Run a number of Gibbs sampling updates to generate each sample.
612 for _ in range(n_thin):
613 self._update(sample_model, dtrajs_lagged_strided, temp_alpha, transition_matrix_prior,
614 initial_distribution_prior)
TypeError: ProgressCallback.__init__() got an unexpected keyword argument 'desc'
in notebook 07, this line :
hmm_4 = pyemma.msm.bayesian_hidden_markov_model(cluster.dtrajs, nstates=4, lag=1, dt_traj='1 ps', nsamples=50)
produces this error:How to solve it?