class AugmentedMSMEstimator

class deeptime.markov.msm.AugmentedMSMEstimator(expectations_by_state, experimental_measurements, experimental_measurement_weights, eps=0.05, support_ci=1.0, maxiter=500, max_cache=3000)

Estimator for augmented Markov state models. [1] This estimator is based on expectation values from experiments. In case the experimental data is a time series matching a discrete time series, a convenience function estimator_from_feature_trajectories() is offered.

Parameters:

• expectations_by_state ((n, k) ndarray) – Expectations by state. n Markov states, k experimental observables; each index is average over members of the Markov state.

• experimental_measurements ((k,) ndarray) – The experimental measurements.

• experimental_measurement_weights ((k,) ndarray) – Experimental measurements weights.

• eps (float, optional, default=0.05) –

  Additional convergence criterion used when some experimental data are outside the support of the simulation. The value of the eps parameter is the threshold of the relative change in the predicted observables as a function of fixed-point iteration:

  \[\mathrm{eps} > \frac{\mid o_{\mathrm{pred}}^{(i+1)}-o_{\mathrm{pred}}^{(i)}\mid }{\sigma}. \]

• support_ci (float, optional, default=1.0) – Confidence interval for determination whether experimental data are inside or outside Markov model support.

• maxiter (int, optional, default=500) – Optional parameter with specifies the maximum number of updates for Lagrange multiplier estimation.

• max_cache (int, optional, default=3000) – Maximum size (in megabytes) of cache when computing R tensor.

References

[1]

Simon Olsson, Hao Wu, Fabian Paul, Cecilia Clementi, and Frank Noé. Combining experimental and simulation data of molecular processes via augmented markov models. Proceedings of the National Academy of Sciences, 114(31):8265–8270, 2017.

Attributes

convergence_criterion_lagrange	Additional convergence criterion used when some experimental data are outside the support of the simulation.
expectations_by_state	The expectations by state (N) for each observable (K).
experimental_measurement_weights	Weights for experimental measurement averages (K).
experimental_measurements	Experimental measurement averages (K).
has_model	Property reporting whether this estimator contains an estimated model.
max_cache	Cache size during computation.
maxiter	Maximum number of Newton iterations.
model	Shortcut to fetch_model().
reversible	If true compute reversible MarkovStateModel, else non-reversible MarkovStateModel
sparse	If true compute count matrix, transition matrix and all derived quantities using sparse matrix algebra.
support_confidence	Confidence interval size for markov states.
uncertainties	Uncertainties based on measurement weights.

Methods

estimator_from_feature_trajectories(...[, ...])	Creates an AMM estimator from discrete trajectories and corresponding experimental data.
fetch_model()	Yields the most recently estimated AMM or None if fit() was not called yet.
fit(data, *args, **kw)	Fits an AMM.
fit_fetch(data, **kwargs)	Fits the internal model on data and subsequently fetches it in one call.
get_params([deep])	Get the parameters.
set_params(**params)	Set the parameters of this estimator.

static estimator_from_feature_trajectories(discrete_trajectories, feature_trajectories, n_states, experimental_measurements, sigmas, eps=0.05, support_ci=1.0, maxiter=500, max_cache=3000)

Creates an AMM estimator from discrete trajectories and corresponding experimental data.

Parameters:

• discrete_trajectories (array_like or list of array_like) – Discrete trajectories, stored as integer ndarrays (arbitrary size) or a single ndarray for only one trajectory.

• feature_trajectories (array_like or list of array_like) – The same shape (number of trajectories and timesteps) as dtrajs. Each timestep in each trajectory should match the shape of the measurements and sigmas, K.

• n_states (int) – Number of markov states in full state space.

• experimental_measurements ((K,) ndarray) – Experimental averages.

• sigmas ((K,) ndarray) – Standard error for each experimental observable.

• eps (float, default = 0.05) – Convergence criterium, see __init__().

• support_ci (float, default=1.0) – Confidence interval, see __init__().

• maxiter (int, optional, default=500) – Maximum number of iterations.

• max_cache (int, optional, default=3000) – Parameter which specifies the maximum size of cache used when performing estimation of AMM, in megabytes.

Returns:

estimator – An estimator parameterized expectations by state based on feature trajectories.

Return type:

AugmentedMSMEstimator

fetch_model() → Optional[AugmentedMSM]

Yields the most recently estimated AMM or None if fit() was not called yet.

Returns:

amm – The AMM instance.

Return type:

AugmentedMSM or None

fit(data, *args, **kw)

Fits an AMM.

Parameters:

• data (TransitionCountModel or (N, N) ndarray) – Count matrix over data.

• *args – scikit-learn compatibility argument

• **kw – scikit-learn compatibility argument

Returns:

self – Reference to self.

Return type:

AugmentedMSMEstimator

fit_fetch(data, **kwargs)

Fits the internal model on data and subsequently fetches it in one call.

Parameters:

• data (array_like) – Data that is used to fit the model.

• **kwargs – Additional arguments to fit().

Returns:

The estimated model.

Return type:

model

get_params(deep=False)

Get the parameters.

Returns:

params – Parameter names mapped to their values.

Return type:

mapping of string to any

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 latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

object

property convergence_criterion_lagrange

Additional convergence criterion used when some experimental data are outside the support of the simulation. The value of the eps parameter is the threshold of the relative change in the predicted observables as a function of fixed-point iteration:

$$ mathrm{eps} > frac{mid o_{mathrm{pred}}^{(i+1)}-o_{mathrm{pred}}^{(i)}mid }{sigma}. $$

property expectations_by_state

The expectations by state (N) for each observable (K).

Type:

(N, K) ndarray

property experimental_measurement_weights

Weights for experimental measurement averages (K).

Type:

(K,) ndarray

property experimental_measurements

Experimental measurement averages (K).

Type:

(K,) ndarray

property has_model: bool

Property reporting whether this estimator contains an estimated model. This assumes that the model is initialized with None otherwise.

Type:

bool

property max_cache

Cache size during computation.

property maxiter

Maximum number of Newton iterations.

property model

Shortcut to fetch_model().

property reversible: bool

If true compute reversible MarkovStateModel, else non-reversible MarkovStateModel

property sparse: bool

If true compute count matrix, transition matrix and all derived quantities using sparse matrix algebra. In this case python sparse matrices will be returned by the corresponding functions instead of numpy arrays. This behavior is suggested for very large numbers of states (e.g. > 4000) because it is likely to be much more efficient.

property support_confidence

Confidence interval size for markov states.

property uncertainties

Uncertainties based on measurement weights.