pyemma.msm.MaximumLikelihoodHMSM¶
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class
pyemma.msm.
MaximumLikelihoodHMSM
(nstates=2, lag=1, stride=1, msm_init='largest-strong', reversible=True, stationary=False, connectivity=None, mincount_connectivity='1/n', observe_nonempty=True, separate=None, dt_traj='1 step', accuracy=0.001, maxit=1000)¶ Maximum likelihood estimator for a Hidden MSM given a MSM
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__init__
(nstates=2, lag=1, stride=1, msm_init='largest-strong', reversible=True, stationary=False, connectivity=None, mincount_connectivity='1/n', observe_nonempty=True, separate=None, dt_traj='1 step', accuracy=0.001, maxit=1000)¶ Maximum likelihood estimator for a Hidden MSM given a MSM
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[lag], s[2 lag], ... s[stride], s[stride + lag], s[stride + 2 lag], ...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 = ‘effective’ uses the largest neglected timescale as an estimate for the correlation time and sets the stride accordingly
- msm_init (str or
MSM
) –MSM object to initialize the estimation, or one of following keywords:
- ‘largest-strong’ or None (default) : Estimate MSM on the largest
- strongly connected set and use spectral clustering to generate an initial HMM
- ‘all’ : Estimate MSM(s) on the full state space to initialize the
- HMM. This estimate maybe weakly connected or disconnected.
- reversible (bool, optional, default = True) – If true compute reversible MSM, else non-reversible MSM
- stationary (bool, optional, default=False) – If True, the initial distribution of hidden states is self-consistently computed as the stationary distribution of the transition matrix. If False, it will be estimated from the starting states. Only set this to true if you’re sure that the observation trajectories are initiated from a global equilibrium distribution.
- connectivity (str, optional, default = None) –
Defines if the resulting HMM will be defined on all hidden states or on a connected subset. Connectivity is defined by counting only transitions with at least mincount_connectivity counts. If a subset of states is used, all estimated quantities (transition matrix, stationary distribution, etc) are only defined on this subset and are correspondingly smaller than nstates. Following modes are available:
- None or ‘all’ : The active set is the full set of states. Estimation is done on all weakly connected subsets separately. The resulting transition matrix may be disconnected.
- ‘largest’ : The active set is the largest reversibly connected set.
- ‘populous’ : The active set is the reversibly connected set with most counts.
- mincount_connectivity (float or '1/n') – minimum number of counts to consider a connection between two states. Counts lower than that will count zero in the connectivity check and may thus separate the resulting transition matrix. The default evaluates to 1/nstates.
- separate (None or iterable of int) – Force the given set of observed states to stay in a separate hidden state. The remaining nstates-1 states will be assigned by a metastable decomposition.
- observe_nonempty (bool) – If True, will restricted the observed states to the states that have at least one observation in the lagged input trajectories. If an initial MSM is given, this option is ignored and the observed subset is always identical to the active set of that MSM.
- dt_traj (str, optional, default='1 step') –
Description of the physical time corresponding to the trajectory time step. May be used by analysis algorithms such as plotting tools to pretty-print the axes. By default ‘1 step’, i.e. there is no physical time unit. Specify by a number, whitespace and unit. Permitted units are (* is an arbitrary string):
‘fs’, ‘femtosecond*’‘ps’, ‘picosecond*’‘ns’, ‘nanosecond*’‘us’, ‘microsecond*’‘ms’, ‘millisecond*’‘s’, ‘second*’ - accuracy (float, optional, default = 1e-3) – convergence threshold for EM iteration. When two the likelihood does not increase by more than accuracy, the iteration is stopped successfully.
- maxit (int, optional, default = 1000) – stopping criterion for EM iteration. When so many iterations are performed without reaching the requested accuracy, the iteration is stopped without convergence (a warning is given)
Methods
__init__
([nstates, lag, stride, msm_init, ...])Maximum likelihood estimator for a Hidden MSM given a MSM 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)Estimates parameters - for compatibility with sklearn. get_model_params
([deep])Get parameters for this model. get_params
([deep])Get parameters for this estimator. 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 given probability distributions set_model_params
([P, pobs, pi, reversible, ...])param P: coarse-grained or hidden transition matrix 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, mincount_connectivity])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. 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. 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
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. metastable_memberships
Computes the memberships of observable states to metastable sets by Bayesian inversion as described in [1]_. metastable_sets
Computes the metastable sets of observable states within each model
The model estimated by this Estimator 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 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. -
P
¶ The transition matrix on the active set.
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active_set
¶ The active set of hidden states on which all hidden state computations are done
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cktest
(mlags=10, conf=0.95, err_est=False, n_jobs=1, show_progress=True)¶ Conducts a Chapman-Kolmogorow test.
Parameters: - mlags (int or int-array, default=10) – multiples of lag times for testing the Model, e.g. range(10). A single int will trigger a range, i.e. mlags=10 maps to mlags=range(10). The setting None will choose mlags automatically according to the longest available trajectory
- conf (float, optional, default = 0.95) – confidence interval
- err_est (bool, default=False) – compute errors also for all estimations (computationally expensive) If False, only the prediction will get error bars, which is often sufficient to validate a model.
- n_jobs (int, default=1) – how many jobs to use during calculation
- show_progress (bool, default=True) – Show progressbars for calculation?
Returns: cktest
Return type: References
This is an adaption of the Chapman-Kolmogorov Test described in detail in [1]_ to Hidden MSMs as described in [2].
[1] Prinz, J H, H Wu, M Sarich, B Keller, M Senne, M Held, J D Chodera, C Schuette and F Noe. 2011. Markov models of molecular kinetics: Generation and validation. J Chem Phys 134: 174105 [2] 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)
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committor_backward
(A, B)¶ Backward committor from set A to set B
Parameters: - A (int or int array) – set of starting states
- B (int or int array) – set of target states
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committor_forward
(A, B)¶ Forward committor (also known as p_fold or splitting probability) from set A to set B
Parameters: - A (int or int array) – set of starting states
- B (int or int array) – set of target states
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correlation
(a, b=None, maxtime=None, k=None, ncv=None)¶ Time-correlation for equilibrium experiment.
In order to simulate a time-correlation experiment (e.g. fluorescence correlation spectroscopy [NDD11], dynamical neutron scattering [LYP13], ...), first compute the mean values of your experimental observable \(a\) by MSM state:
\[a_i = \frac{1}{N_i} \sum_{x_t \in S_i} f(x_t)\]where \(S_i\) is the set of configurations belonging to MSM state \(i\) and \(f()\) is a function that computes the experimental observable of interest for configuration \(x_t\). If a cross-correlation function is wanted, also apply the above computation to a second experimental observable \(b\).
Then the accurate (i.e. without statistical error) autocorrelation function of \(f(x_t)\) given the Markov model is computed by correlation(a), and the accurate cross-correlation function is computed by correlation(a,b). This is done by evaluating the equation
\[\begin{split}acf_a(k\tau) &= \mathbf{a}^\top \mathrm{diag}(\boldsymbol{\pi}) \mathbf{P(\tau)}^k \mathbf{a} \\ ccf_{a,b}(k\tau) &= \mathbf{a}^\top \mathrm{diag}(\boldsymbol{\pi}) \mathbf{P(\tau)}^k \mathbf{b}\end{split}\]where \(acf\) stands for autocorrelation function and \(ccf\) stands for cross-correlation function, \(\mathbf{P(\tau)}\) is the transition matrix at lag time \(\tau\), \(\boldsymbol{\pi}\) is the equilibrium distribution of \(\mathbf{P}\), and \(k\) is the time index.
Note that instead of using this method you could generate a long synthetic trajectory from the MSM and then estimating the time-correlation of your observable(s) directly from this trajectory. However, there is no reason to do this because the present method does that calculation without any sampling, and only in the limit of an infinitely long synthetic trajectory the two results will agree exactly. The correlation function computed by the present method still has statistical uncertainty from the fact that the underlying MSM transition matrix has statistical uncertainty when being estimated from data, but there is no additional (and unnecessary) uncertainty due to synthetic trajectory generation.
Parameters: - a ((n,) ndarray) – Observable, represented as vector on state space
- maxtime (int or float) – Maximum time (in units of the input trajectory time step) until which the correlation function will be evaluated. Internally, the correlation function can only be computed in integer multiples of the Markov model lag time, and therefore the actual last time point will be computed at \(\mathrm{ceil}(\mathrm{maxtime} / \tau)\) By default (None), the maxtime will be set equal to the 5 times the slowest relaxation time of the MSM, because after this time the signal is almost constant.
- b ((n,) ndarray (optional)) – Second observable, for cross-correlations
- k (int (optional)) – Number of eigenvalues and eigenvectors to use for computation. This option is only relevant for sparse matrices and long times for which an eigenvalue decomposition will be done instead of using the matrix power.
- ncv (int (optional)) – Only relevant for sparse matrices and large lag times where the relaxation will be computed using an eigenvalue decomposition. The number of Lanczos vectors generated, ncv must be greater than k; it is recommended that ncv > 2*k.
Returns: - times (ndarray (N)) – Time points (in units of the input trajectory time step) at which the correlation has been computed
- correlations (ndarray (N)) – Correlation values at given times
Examples
This example computes the autocorrelation function of a simple observable on a three-state Markov model and plots the result using matplotlib:
>>> import numpy as np >>> import pyemma.msm as msm >>> >>> P = np.array([[0.99, 0.01, 0], [0.01, 0.9, 0.09], [0, 0.1, 0.9]]) >>> a = np.array([0.0, 0.5, 1.0]) >>> M = msm.markov_model(P) >>> times, acf = M.correlation(a) >>> >>> import matplotlib.pylab as plt >>> plt.plot(times, acf)
References
[NDD11] Noe, F., S. Doose, I. Daidone, M. Loellmann, J. D. Chodera, M. Sauer and J. C. Smith. 2011 Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. Proc. Natl. Acad. Sci. USA 108, 4822-4827. [LYP13] Lindner, B., Z. Yi, J.-H. Prinz, J. C. Smith and F. Noe. 2013. Dynamic Neutron Scattering from Conformational Dynamics I: Theory and Markov models. J. Chem. Phys. 139, 175101.
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discrete_trajectories_full
¶ A list of integer arrays with the original trajectories.
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discrete_trajectories_lagged
¶ Transformed original trajectories that are used as an input into the HMM estimation
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discrete_trajectories_obs
¶ A list of integer arrays with the discrete trajectories mapped to the observation mode used. When using observe_active = True, the indexes will be given on the MSM active set. Frames that are not in the observation set will be -1. When observe_active = False, this attribute is identical to discrete_trajectories_full
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dt_model
¶ Description of the physical time corresponding to the lag.
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dtrajs_full
¶ A list of integer arrays with the original trajectories.
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dtrajs_lagged
¶ Transformed original trajectories that are used as an input into the HMM estimation
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dtrajs_obs
¶ A list of integer arrays with the discrete trajectories mapped to the observation mode used. When using observe_active = True, the indexes will be given on the MSM active set. Frames that are not in the observation set will be -1. When observe_active = False, this attribute is identical to discrete_trajectories_full
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eigenvalues
(k=None)¶ Compute the transition matrix eigenvalues
Parameters: k (int) – number of eigenvalues to be returned. By default will return all available eigenvalues Returns: ts – transition matrix eigenvalues \(\lambda_i, i = 1, ..., k\)., sorted by descending norm. Return type: ndarray(k,)
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eigenvectors_left
(k=None)¶ Compute the left transition matrix eigenvectors
Parameters: k (int) – number of eigenvectors to be returned. By default all available eigenvectors. Returns: L – left eigenvectors in a row matrix. l_ij is the j’th component of the i’th left eigenvector Return type: ndarray(k,n)
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eigenvectors_right
(k=None)¶ Compute the right transition matrix eigenvectors
Parameters: k (int) – number of eigenvectors to be computed. By default all available eigenvectors. Returns: R – right eigenvectors in a column matrix. r_ij is the i’th component of the j’th right eigenvector Return type: ndarray(n,k)
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estimate
(X, **params)¶ Estimates the model given the data X
Parameters: - X (object) – A reference to the data from which the model will be estimated
- params (dict) – New estimation parameter values. The parameters must that have been announced in the __init__ method of this estimator. The present settings will overwrite the settings of parameters given in the __init__ method, i.e. the parameter values after this call will be those that have been used for this estimation. Use this option if only one or a few parameters change with respect to the __init__ settings for this run, and if you don’t need to remember the original settings of these changed parameters.
Returns: estimator – The estimated estimator with the model being available.
Return type: object
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expectation
(a)¶ Equilibrium expectation value of a given observable.
Parameters: a ((n,) ndarray) – Observable vector on the MSM state space Returns: val – Equilibrium expectation value fo the given observable Return type: float Notes
The equilibrium expectation value of an observable \(a\) is defined as follows
\[\mathbb{E}_{\mu}[a] = \sum_i \pi_i a_i\]\(\pi=(\pi_i)\) is the stationary vector of the transition matrix \(P\).
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fingerprint_correlation
(a, b=None, k=None, ncv=None)¶ Dynamical fingerprint for equilibrium time-correlation experiment.
Parameters: - a ((n,) ndarray) – Observable, represented as vector on MSM state space
- b ((n,) ndarray, optional) – Second observable, for cross-correlations
- k (int, optional) – Number of eigenvalues and eigenvectors to use for computation. This option is only relevant for sparse matrices and long times for which an eigenvalue decomposition will be done instead of using the matrix power
- ncv (int, optional) – Only relevant for sparse matrices and large lag times, where the relaxation will be computed using an eigenvalue decomposition. The number of Lanczos vectors generated, ncv must be greater than k; it is recommended that ncv > 2*k
Returns: - timescales ((k,) ndarray) – Time-scales (in units of the input trajectory time step) of the transition matrix
- amplitudes ((k,) ndarray) – Amplitudes for the correlation experiment
References
Spectral densities are commonly used in spectroscopy. Dynamical fingerprints are a useful representation for computational spectroscopy results and have been introduced in [NDD11].
[NDD11] Noe, F, S Doose, I Daidone, M Loellmann, M Sauer, J D Chodera and J Smith. 2010. Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. PNAS 108 (12): 4822-4827.
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fingerprint_relaxation
(p0, a, k=None, ncv=None)¶ Dynamical fingerprint for perturbation/relaxation experiment.
Parameters: - p0 ((n,) ndarray) – Initial distribution for a relaxation experiment
- a ((n,) ndarray) – Observable, represented as vector on state space
- k (int, optional) – Number of eigenvalues and eigenvectors to use for computation
- ncv (int, optional) – Only relevant for sparse matrices and large lag times, where the relaxation will be computes using an eigenvalue decomposition. The number of Lanczos vectors generated, ncv must be greater than k; it is recommended that ncv > 2*k
Returns: - timescales ((k,) ndarray) – Time-scales (in units of the input trajectory time step) of the transition matrix
- amplitudes ((k,) ndarray) – Amplitudes for the relaxation experiment
References
Spectral densities are commonly used in spectroscopy. Dynamical fingerprints are a useful representation for computational spectroscopy results and have been introduced in [NDD11].
[NDD11] Noe, F, S Doose, I Daidone, M Loellmann, M Sauer, J D Chodera and J Smith. 2010. Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. PNAS 108 (12): 4822-4827.
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fit
(X)¶ Estimates parameters - for compatibility with sklearn.
Parameters: X (object) – A reference to the data from which the model will be estimated Returns: estimator – The estimator (self) with estimated model. Return type: object
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get_model_params
(deep=True)¶ Get parameters for this model.
Parameters: deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params – Parameter names mapped to their values. Return type: mapping of string to any
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get_params
(deep=True)¶ Get parameters for this estimator.
Parameters: deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params – Parameter names mapped to their values. Return type: mapping of string to any
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is_reversible
¶ Returns whether the MSM is reversible
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is_sparse
¶ Returns whether the MSM is sparse
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lagtime
¶ The lag time in steps
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lifetimes
¶ Lifetimes of states of the hidden transition matrix
Returns: l – state lifetimes in units of the input trajectory time step, defined by \(-\tau / ln \mid p_{ii} \mid, i = 1,...,nstates\), where \(p_{ii}\) are the diagonal entries of the hidden transition matrix. Return type: ndarray(nstates)
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logger
¶ The logger for this class instance
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metastable_assignments
¶ Computes the assignment to metastable sets for observable states
Notes
This is only recommended for visualization purposes. You cannot compute any actual quantity of the coarse-grained kinetics without employing the fuzzy memberships!
Returns: For each observable state, the metastable state it is located in. Return type: ndarray((n) ,dtype=int)
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metastable_distributions
¶ - Returns the output probability distributions. Identical to
observation_probabilities()
Returns: Pout – output probability matrix from hidden to observable discrete states Return type: ndarray (m,n) See also
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metastable_memberships
¶ - Computes the memberships of observable states to metastable sets by
- Bayesian inversion as described in [1]_.
Returns: M – A matrix containing the probability or membership of each observable state to be assigned to each metastable or hidden state. The row sums of M are 1. Return type: ndarray((n,m)) 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)
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metastable_sets
¶ - Computes the metastable sets of observable states within each
- metastable set
Notes
This is only recommended for visualization purposes. You cannot compute any actual quantity of the coarse-grained kinetics without employing the fuzzy memberships!
Returns: sets – A list of length equal to metastable states. Each element is an array with observable state indexes contained in it Return type: list of int-arrays
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mfpt
(A, B)¶ Mean first passage times from set A to set B, in units of the input trajectory time step
Parameters: - A (int or int array) – set of starting states
- B (int or int array) – set of target states
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model
¶ The model estimated by this Estimator
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name
¶ The name of this instance
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neig
¶ number of eigenvalues to compute.
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nstates
¶ Number of active states on which all computations and estimations are done
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nstates_obs
¶ Number of states in discrete trajectories
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observable_set
¶ The active set of states on which all computations and estimations will be done
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observable_state_indexes
¶ Ensures that the observable states are indexed and returns the indices
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observation_probabilities
¶ returns the output probability matrix
Returns: Pout – output probability matrix from hidden to observable discrete states Return type: ndarray (m,n)
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pcca
(m)¶ Runs PCCA++ [1]_ to compute a metastable decomposition of MSM states
After calling this method you can access
metastable_memberships()
,metastable_distributions()
,metastable_sets()
andmetastable_assignments()
.Parameters: m (int) – Number of metastable sets Returns: pcca_obj – An object containing all PCCA quantities. However, you can also ignore this return value and instead retrieve the quantities of your interest with the following MSM functions: metastable_memberships()
,metastable_distributions()
,metastable_sets()
andmetastable_assignments()
.Return type: PCCA
Notes
If you coarse grain with PCCA++, the order of the obtained memberships might not be preserved. This also applies for
metastable_memberships()
,metastable_distributions()
,metastable_sets()
,metastable_assignments()
References
[1] Roeblitz, S and M Weber. 2013. Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification. Advances in Data Analysis and Classification 7 (2): 147-179
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pi
¶ The stationary distribution on the MSM states
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propagate
(p0, k)¶ Propagates the initial distribution p0 k times
Computes the product
\[p_k = p_0^T P^k\]If the lag time of transition matrix \(P\) is \(\tau\), this will provide the probability distribution at time \(k \tau\).
Parameters: - p0 (ndarray(n)) – Initial distribution. Vector of size of the active set.
- k (int) – Number of time steps
Returns: pk – Distribution after k steps. Vector of size of the active set.
Return type: ndarray(n)
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relaxation
(p0, a, maxtime=None, k=None, ncv=None)¶ Simulates a perturbation-relaxation experiment.
In perturbation-relaxation experiments such as temperature-jump, pH-jump, pressure jump or rapid mixing experiments, an ensemble of molecules is initially prepared in an off-equilibrium distribution and the expectation value of some experimental observable is then followed over time as the ensemble relaxes towards equilibrium.
In order to simulate such an experiment, first determine the distribution of states at which the experiment is started, \(p_0\) and compute the mean values of your experimental observable \(a\) by MSM state:
\[a_i = \frac{1}{N_i} \sum_{x_t \in S_i} f(x_t)\]where \(S_i\) is the set of configurations belonging to MSM state \(i\) and \(f()\) is a function that computes the experimental observable of interest for configuration \(x_t\).
Then the accurate (i.e. without statistical error) time-dependent expectation value of \(f(x_t)\) given the Markov model is computed by relaxation(p0, a). This is done by evaluating the equation
\[E_a(k\tau) = \mathbf{p_0}^{\top} \mathbf{P(\tau)}^k \mathbf{a}\]where \(E\) stands for the expectation value that relaxes to its equilibrium value that is identical to expectation(a), \(\mathbf{P(\tau)}\) is the transition matrix at lag time \(\tau\), \(\boldsymbol{\pi}\) is the equilibrium distribution of \(\mathbf{P}\), and \(k\) is the time index.
Note that instead of using this method you could generate many synthetic trajectory from the MSM with starting points drawn from the initial distribution and then estimating the time-dependent expectation value by an ensemble average. However, there is no reason to do this because the present method does that calculation without any sampling, and only in the limit of an infinitely many trajectories the two results will agree exactly. The relaxation function computed by the present method still has statistical uncertainty from the fact that the underlying MSM transition matrix has statistical uncertainty when being estimated from data, but there is no additional (and unnecessary) uncertainty due to synthetic trajectory generation.
Parameters: - p0 ((n,) ndarray) – Initial distribution for a relaxation experiment
- a ((n,) ndarray) – Observable, represented as vector on state space
- maxtime (int or float, optional) – Maximum time (in units of the input trajectory time step) until which the correlation function will be evaluated. Internally, the correlation function can only be computed in integer multiples of the Markov model lag time, and therefore the actual last time point will be computed at \(\mathrm{ceil}(\mathrm{maxtime} / \tau)\). By default (None), the maxtime will be set equal to the 5 times the slowest relaxation time of the MSM, because after this time the signal is constant.
- k (int (optional)) – Number of eigenvalues and eigenvectors to use for computation
- ncv (int (optional)) – Only relevant for sparse matrices and large lag times, where the relaxation will be computes using an eigenvalue decomposition. The number of Lanczos vectors generated, ncv must be greater than k; it is recommended that ncv > 2*k
Returns: - times (ndarray (N)) – Time points (in units of the input trajectory time step) at which the relaxation has been computed
- res (ndarray) – Array of expectation value at given times
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reversible
¶ Returns whether the MSM is reversible
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sample_by_observation_probabilities
(nsample)¶ Generates samples according to given probability distributions
Parameters: - distributions (list or array of ndarray ( (n) )) – m distributions over states. Each distribution must be of length n and must sum up to 1.0
- nsample (int) – Number of samples per distribution. If replace = False, the number of returned samples per state could be smaller if less than nsample indexes are available for a state.
Returns: indexes – List of the sampled indices by distribution. Each element is an index array with a number of rows equal to nsample, with rows consisting of a tuple (i, t), where i is the index of the trajectory and t is the time index within the trajectory.
Return type: length m list of ndarray( (nsample, 2) )
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set_model_params
(P=None, pobs=None, pi=None, reversible=None, dt_model='1 step', neig=None)¶ Parameters: - P (ndarray(m,m)) – coarse-grained or hidden transition matrix
- pobs (ndarray (m,n)) – observation probability matrix from hidden to observable discrete states
- pi (ndarray(m), optional, default=None) – stationary or distribution. Can be optionally given in case if it was already computed, e.g. by the estimator.
- dt_model (str, optional, default='1 step') – time step of the model
- neig (int or None) – The number of eigenvalues / eigenvectors to be kept. If set to None, all eigenvalues will be used.
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set_params
(**params)¶ Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. :returns: :rtype: self
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simulate
(N, start=None, stop=None, dt=1)¶ Generates a realization of the Hidden Markov Model
Parameters: - N (int) – trajectory length in steps of the lag time
- start (int, optional, default = None) – starting hidden state. If not given, will sample from the stationary distribution of the hidden transition matrix.
- stop (int or int-array-like, optional, default = None) – stopping hidden set. If given, the trajectory will be stopped before N steps once a hidden state of the stop set is reached
- dt (int) – trajectory will be saved every dt time steps. Internally, the dt’th power of P is taken to ensure a more efficient simulation.
Returns: - htraj ((N/dt, ) ndarray) – The hidden state trajectory with length N/dt
- otraj ((N/dt, ) ndarray) – The observable state discrete trajectory with length N/dt
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sparse
¶ Returns whether the MSM is sparse
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stationary_distribution
¶ The stationary distribution on the MSM states
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submodel
(states=None, obs=None, mincount_connectivity='1/n')¶ Returns a HMM with restricted state space
Parameters: - states (None, str or int-array) – Hidden states to restrict the model to. In addition to specifying the subset, possible options are: * None : all states - don’t restrict * ‘populous-strong’ : strongly connected subset with maximum counts * ‘populous-weak’ : weakly connected subset with maximum counts * ‘largest-strong’ : strongly connected subset with maximum size * ‘largest-weak’ : weakly connected subset with maximum size
- obs (None, str or int-array) – Observed states to restrict the model to. In addition to specifying an array with the state labels to be observed, possible options are: * None : all states - don’t restrict * ‘nonempty’ : all states with at least one observation in the estimator
- mincount_connectivity (float or '1/n') – minimum number of counts to consider a connection between two states. Counts lower than that will count zero in the connectivity check and may thus separate the resulting transition matrix. Default value: 1/nstates.
Returns: hmm – The restricted HMM.
Return type: HMM
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submodel_disconnect
(mincount_connectivity='1/n')¶ Disconnects sets of hidden states that are barely connected
Runs a connectivity check excluding all transition counts below mincount_connectivity. The transition matrix and stationary distribution will be re-estimated. Note that the resulting transition matrix may have both strongly and weakly connected subsets.
Parameters: mincount_connectivity (float or '1/n') – minimum number of counts to consider a connection between two states. Counts lower than that will count zero in the connectivity check and may thus separate the resulting transition matrix. The default evaluates to 1/nstates. Returns: hmm – The restricted HMM. Return type: HMM
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submodel_largest
(strong=True, mincount_connectivity='1/n')¶ Returns the largest connected sub-HMM (convenience function)
Returns: hmm – The restricted HMM. Return type: HMM
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submodel_populous
(strong=True, mincount_connectivity='1/n')¶ Returns the most populous connected sub-HMM (convenience function)
Returns: hmm – The restricted HMM. Return type: HMM
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timescales
(k=None)¶ The relaxation timescales corresponding to the eigenvalues
Parameters: k (int) – number of timescales to be returned. By default all available eigenvalues, minus 1. Returns: ts – relaxation timescales in units of the input trajectory time step, defined by \(-\tau / ln | \lambda_i |, i = 2,...,k+1\). Return type: ndarray(m)
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timestep_model
¶ Physical time corresponding to one transition matrix step, e.g. ‘10 ps’
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trajectory_weights
()¶ Uses the HMSM to assign a probability weight to each trajectory frame.
This is a powerful function for the calculation of arbitrary observables in the trajectories one has started the analysis with. The stationary probability of the MSM will be used to reweigh all states. Returns a list of weight arrays, one for each trajectory, and with a number of elements equal to trajectory frames. Given \(N\) trajectories of lengths \(T_1\) to \(T_N\), this function returns corresponding weights:
\[(w_{1,1}, ..., w_{1,T_1}), (w_{N,1}, ..., w_{N,T_N})\]that are normalized to one:
\[\sum_{i=1}^N \sum_{t=1}^{T_i} w_{i,t} = 1\]Suppose you are interested in computing the expectation value of a function \(a(x)\), where \(x\) are your input configurations. Use this function to compute the weights of all input configurations and obtain the estimated expectation by:
\[\langle a \rangle = \sum_{i=1}^N \sum_{t=1}^{T_i} w_{i,t} a(x_{i,t})\]Or if you are interested in computing the time-lagged correlation between functions \(a(x)\) and \(b(x)\) you could do:
\[\langle a(t) b(t+\tau) \rangle_t = \sum_{i=1}^N \sum_{t=1}^{T_i} w_{i,t} a(x_{i,t}) a(x_{i,t+\tau})\]Returns: - The normalized trajectory weights. Given \(N\) trajectories of lengths \(T_1\) to \(T_N\),
- returns the corresponding weights
- .. math:: – (w_{1,1}, ..., w_{1,T_1}), (w_{N,1}, ..., w_{N,T_N})
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transition_matrix
¶ The transition matrix on the active set.
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transition_matrix_obs
(k=1)¶ Computes the transition matrix between observed states
Transition matrices for longer lag times than the one used to parametrize this HMSM can be obtained by setting the k option. Note that a HMSM is not Markovian, thus we cannot compute transition matrices at longer lag times using the Chapman-Kolmogorow equality. I.e.:
\[P (k \tau) \neq P^k (\tau)\]This function computes the correct transition matrix using the metastable (coarse) transition matrix \(P_c\) as:
\[P (k \tau) = {\Pi}^-1 \chi^{\top} ({\Pi}_c) P_c^k (\tau) \chi\]where \(\chi\) is the output probability matrix, \(\Pi_c\) is a diagonal matrix with the metastable-state (coarse) stationary distribution and \(\Pi\) is a diagonal matrix with the observable-state stationary distribution.
Parameters: k (int, optional, default=1) – Multiple of the lag time for which the By default (k=1), the transition matrix at the lag time used to construct this HMSM will be returned. If a higher power is given,
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update_model_params
(**params)¶ Update given model parameter if they are set to specific values
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