pyemma.msm.BayesianHMSM

class pyemma.msm.BayesianHMSM(*args, **kwargs)

Estimator for a Bayesian Hidden Markov state model

__init__(nstates=2, lag=1, stride='effective', p0_prior='mixed', transition_matrix_prior='mixed', nsamples=100, init_hmsm=None, reversible=True, stationary=False, connectivity='largest', mincount_connectivity='1/n', separate=None, observe_nonempty=True, dt_traj='1 step', conf=0.95, store_hidden=False, show_progress=True)

Estimator for a Bayesian HMSM

Parameters

• nstates (int, optional, default=2) – number of hidden states

• lag (int, optional, default=1) – lagtime to estimate the HMSM at

• stride (str or int, default=1) –

  stride between two lagged trajectories extracted from the input trajectories. Given trajectory s[t], stride and lag will result in trajectories

  s[0], s[tau], s[2 tau], … s[stride], s[stride + tau], s[stride + 2 tau], …

  Setting stride = 1 will result in using all data (useful for maximum likelihood estimator), while a Bayesian estimator requires a longer stride in order to have statistically uncorrelated trajectories. Setting stride = None ‘effective’ uses the largest neglected timescale as an estimate for the correlation time and sets the stride accordingly.

• p0_prior (None, str, float or ndarray(n)) –

  Prior for the initial distribution of the HMM. Will only be active if stationary=False (stationary=True means that p0 is identical to the stationary distribution of the transition matrix). Currently implements different versions of the Dirichlet prior that is conjugate to the Dirichlet distribution of p0. p0 is sampled from:

  where \(n_i\) are the number of times a hidden trajectory was in state \(i\) at time step 0 and \(a_i\) is the prior count. Following options are available:

  • ’mixed’ (default), \(a_i = p_{0,init}\), where \(p_{0,init}\) is the initial distribution of initial_model.

  • ndarray(n) or float, the given array will be used as A.

  • ’uniform’, \(a_i = 1\)

  • None, \(a_i = 0\). This option ensures coincidence between sample mean an MLE. Will sooner or later lead to sampling problems, because as soon as zero trajectories are drawn from a given state, the sampler cannot recover and that state will never serve as a starting state subsequently. Only recommended in the large data regime and when the probability to sample zero trajectories from any state is negligible.

• transition_matrix_prior (str or ndarray(n, n)) –

  Prior for the HMM transition matrix. Currently implements Dirichlet priors if reversible=False and reversible transition matrix priors as described in 3 if reversible=True. For the nonreversible case the posterior of transition matrix \(P\) is:

  where \(c_{ij}\) are the number of transitions found for hidden trajectories and \(b_{ij}\) are prior counts.

  • ’mixed’ (default), \(b_{ij} = p_{ij,init}\), where \(p_{ij,init}\) is the transition matrix of initial_model. That means one prior count will be used per row.

  • ndarray(n, n) or broadcastable, the given array will be used as B.

  • ’uniform’, \(b_{ij} = 1\)

  • None, \(b_ij = 0\). This option ensures coincidence between sample mean an MLE. Will sooner or later lead to sampling problems, because as soon as a transition \(ij\) will not occur in a sample, the sampler cannot recover and that transition will never be sampled again. This option is not recommended unless you have a small HMM and a lot of data.

• init_hmsm (HMSM, default=None) – Single-point estimate of HMSM object around which errors will be evaluated. If None is give an initial estimate will be automatically generated using the given parameters.

• store_hidden (bool, optional, default=False) – store hidden trajectories in sampled HMMs

• show_progress (bool, default=True) – Show progressbars for calculation?

References

1

F. Noe, H. Wu, J.-H. Prinz and N. Plattner: Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules. J. Chem. Phys. 139, 184114 (2013)

2

J. D. Chodera Et Al: Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty. arXiv:1108.1430 (2011)

3

Trendelkamp-Schroer, B., H. Wu, F. Paul and F. Noe: Estimation and uncertainty of reversible Markov models. J. Chem. Phys. 143, 174101 (2015).

Methods

_Loggable__create_logger()
_ProgressReporterMixin__check_stage_registered(stage)
_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()
__gt__(value, /)	Return self>value.
__init__([nstates, lag, stride, p0_prior, …])	Estimator for a Bayesian HMSM
__init_subclass__(*args, **kwargs)	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__my_getstate__()
__my_setstate__(state)
__ne__(value, /)	Return self!=value.
__new__(cls, *args, **kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_assert_in_active(A)	Checks if set A is within the active set
_assert_metastable()	Tests if pcca object is available, or else raises a ValueError.
_check_estimated()
_check_samples_available()
_cleanup_logger(logger_id, logger_name)
_committor_backward(P, A, B[, mu])
_committor_forward(P, A, B)
_compute_eigendecomposition(neig)	Conducts the eigenvalue decomposition and stores k eigenvalues, left and right eigenvectors
_compute_eigenvalues(neig)	Conducts the eigenvalue decomposition and stores k eigenvalues, left and right eigenvectors
_ensure_eigendecomposition([neig])	Ensures that eigendecomposition has been performed with at least neig eigenpairs
_ensure_eigenvalues([neig])	Ensures that at least neig eigenvalues have been computed
_estimate(dtrajs)
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_get_model_param_names()	Get parameter names for the model
_get_param_names()	Get parameter names for the estimator
_get_private_field(cls, name[, default])
_get_serialize_fields(cls)
_get_state_of_serializeable_fields(klass, state)	:return a dictionary {k:v} for k in self.serialize_fields and v=getattr(self, k)
_get_version(cls[, require])
_get_version_for_class_from_state(state, klass)	retrieves the version of the current klass from the state mapping from old locations to new ones.
_logger_is_active(level)	@param level: int log level (debug=10, info=20, warn=30, error=40, critical=50)
_mfpt(P, A, B[, mu])
_progress_context([stage])	param stage
_progress_force_finish([stage, description])	forcefully finish the progress for given stage
_progress_register(amount_of_work[, …])	Registers a progress which can be reported/displayed via a progress bar.
_progress_set_description(stage, description)	set description of an already existing progress
_progress_update(numerator_increment[, …])	Updates the progress.
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
cktest([mlags, conf, err_est, n_jobs, …])	Conducts a Chapman-Kolmogorow test.
committor_backward(A, B)	Backward committor from set A to set B
committor_forward(A, B)	Forward committor (also known as p_fold or splitting probability) from set A to set B
correlation(a[, b, maxtime, k, ncv])	Time-correlation for equilibrium experiment.
eigenvalues([k])	Compute the transition matrix eigenvalues
eigenvectors_left([k])	Compute the left transition matrix eigenvectors
eigenvectors_right([k])	Compute the right transition matrix eigenvectors
estimate(X, **params)	Estimates the model given the data X
expectation(a)	Equilibrium expectation value of a given observable.
fingerprint_correlation(a[, b, k, ncv])	Dynamical fingerprint for equilibrium time-correlation experiment.
fingerprint_relaxation(p0, a[, k, ncv])	Dynamical fingerprint for perturbation/relaxation experiment.
fit(X[, y])	Estimates parameters - for compatibility with sklearn.
get_model_params([deep])	Get parameters for this model.
get_params([deep])	Get parameters for this estimator.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
mfpt(A, B)	Mean first passage times from set A to set B, in units of the input trajectory time step
pcca(m)	Runs PCCA++ 1 to compute a metastable decomposition of MSM states
propagate(p0, k)	Propagates the initial distribution p0 k times
relaxation(p0, a[, maxtime, k, ncv])	Simulates a perturbation-relaxation experiment.
sample_by_observation_probabilities(nsample)	Generates samples according to the current observation probability distribution
sample_conf(f, *args, **kwargs)	Sample confidence interval of numerical method f over all samples
sample_f(f, *args, **kwargs)	Evaluated method f for all samples
sample_mean(f, *args, **kwargs)	Sample mean of numerical method f over all samples
sample_std(f, *args, **kwargs)	Sample standard deviation of numerical method f over all samples
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_model_params([samples, conf, P, pobs, …])	param samples sampled MSMs
set_params(**params)	Set the parameters of this estimator.
simulate(N[, start, stop, dt])	Generates a realization of the Hidden Markov Model
submodel([states, obs, …])	Returns a HMM with restricted state space
submodel_disconnect([mincount_connectivity])	Disconnects sets of hidden states that are barely connected
submodel_largest([strong, mincount_connectivity])	Returns the largest connected sub-HMM (convenience function)
submodel_populous([strong, …])	Returns the most populous connected sub-HMM (convenience function)
timescales([k])	The relaxation timescales corresponding to the eigenvalues
trajectory_weights()	Uses the HMSM to assign a probability weight to each trajectory frame.
transition_matrix_obs([k])	Computes the transition matrix between observed states
update_model_params(**params)	Update given model parameter if they are set to specific values

Attributes

P	The transition matrix on the active set.
_BayesianHMSM__serialize_fields
_BayesianHMSM__serialize_version
_Estimator__serialize_fields
_HMSM__serialize_version
_Loggable__ids
_Loggable__refs
_MSM__serialize_fields
_MSM__serialize_version
_MaximumLikelihoodHMSM__serialize_fields
_MaximumLikelihoodHMSM__serialize_version
_SampledHMSM__serialize_version
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
__dict__
__doc__
__hash__
__module__
__weakref__	list of weak references to the object (if defined)
_estimated
_loglevel_CRITICAL
_loglevel_DEBUG
_loglevel_ERROR
_loglevel_INFO
_loglevel_WARN
_pg_threshold
_prog_rep_callbacks
_prog_rep_descriptions
_prog_rep_progressbars
_progress_num_registered
_progress_registered_stages
_save_data_producer
active_set	The active set of hidden states on which all hidden state computations are done
discrete_trajectories_full	A list of integer arrays with the original trajectories.
discrete_trajectories_lagged	Transformed original trajectories that are used as an input into the HMM estimation
discrete_trajectories_obs	A list of integer arrays with the discrete trajectories mapped to the observation mode used.
dt_model	Description of the physical time corresponding to the lag.
dt_traj
dtrajs_full	A list of integer arrays with the original trajectories.
dtrajs_lagged	Transformed original trajectories that are used as an input into the HMM estimation
dtrajs_obs	A list of integer arrays with the discrete trajectories mapped to the observation mode used.
eigenvectors_left_obs
eigenvectors_right_obs
init_hmsm
is_reversible	Returns whether the MSM is reversible
is_sparse	Returns whether the MSM is sparse
lagtime	The lag time in steps
lifetimes	Lifetimes of states of the hidden transition matrix
logger	The logger for this class instance
metastable_assignments	Computes the assignment to metastable sets for observable states
metastable_distributions	Returns the output probability distributions. Identical to
metastable_memberships	Computes the memberships of observable states to metastable sets by
metastable_sets	Computes the metastable sets of observable states within each
model	The model estimated by this Estimator
msm_init
n_metastable	Number of states chosen for PCCA++ computation.
name	The name of this instance
neig	number of eigenvalues to compute.
nstates	Number of active states on which all computations and estimations are done
nstates_obs	Number of states in discrete trajectories
observable_set	The active set of states on which all computations and estimations will be done
observable_state_indexes	Ensures that the observable states are indexed and returns the indices
observation_probabilities	returns the output probability matrix
pi	The stationary distribution on the MSM states
reversible	Returns whether the MSM is reversible
samples
show_progress	whether to show the progress of heavy calculations on this object.
sparse	Returns whether the MSM is sparse
stationary_distribution	The stationary distribution on the MSM states
stationary_distribution_obs
timestep_model	Physical time corresponding to one transition matrix step, e.g.
transition_matrix	The transition matrix on the active set.