# msmtools.estimation.rate_matrix¶

msmtools.estimation.rate_matrix(C, dt=1.0, method='KL', sparsity=None, t_agg=None, pi=None, tol=10000000.0, K0=None, maxiter=100000, on_error='raise')

Estimate a reversible rate matrix from a count matrix.

Parameters: C ((N,N) ndarray) – count matrix at a lag time dt dt (float, optional, default=1.0) – lag time that was used to estimate C method (str, one of {'KL', 'CVE', 'pseudo', 'truncated_log'}) – Method to use for estimation of the rate matrix. ’pseudo’ selects the pseudo-generator. A reversible transition matrix T is estimated and $$(T-Id)/d$$ is returned as the rate matrix. ’truncated_log’ selects the truncated logarithm . A reversible transition matrix T is estimated and $$max(logm(T*T)/(2dt),0)$$ is returned as the rate matrix. logm is the matrix logarithm and the maximum is taken element-wise. ’CVE’ selects the algorithm of Crommelin and Vanden-Eijnden . It consists of minimizing the following objective function: $f(K)=\sum_{ij}\left(\sum_{kl} U_{ik}^{-1}K_{kl}U_{lj}-L_{ij}\right)^2 \left|\Lambda_{i}\Lambda_{j}\right|$ where $$\Lambda_i$$ are the eigenvalues of $$T$$ and $$U$$ is the matrix of its (right) eigenvectors; $$L_{ij}=\delta_{ij}\frac{1}{\tau}\log\left|\Lambda_i\right|$$. $$T$$ is computed from C using the reversible maximum likelihood estimator. ’KL’ selects the algorihtm of Kalbfleisch and Lawless . It consists of maximizing the following log-likelihood: $f(K)=\log L=\sum_{ij}C_{ij}\log(e^{K\Delta t})_{ij}$ where $$C_{ij}$$ are the transition counts at a lag-time $$\Delta t$$. Here $$e$$ is the matrix exponential and the logarithm is taken element-wise. sparsity ((N,N) ndarray or None, optional, default=None) – If sparsity is None, a fully occupied rate matrix will be estimated. Alternatively, with the methods ‘CVE’ and ‘KL’ a ndarray of the same shape as C can be supplied. If sparsity[i,j]=0 and sparsity[j,i]=0 the rate matrix elements $$K_{ij}$$ and $$K_{ji}$$ will be constrained to zero. t_agg (float, optional) – the aggregated simulation time; by default this is the total number of transition counts times the lag time (no sliding window counting). This value is used to compute the lower bound on the transition rate (that are not zero). If sparsity is None, this value is ignored. pi – the stationary vector of the desired rate matrix K. If no pi is given, the function takes the stationary vector of the MLE reversible T matrix that is computed from C. tol (float, optional, default = 1.0E7) – Tolerance of the quasi-Newton algorithm that is used to minimize the objective function. This is passed as the factr parameter to scipy.optimize.fmin_l_bfgs_b. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. maxiter (int, optional, default = 100000) – Minimization of the objective function will do at most this number of steps. on_error (string, optional, default = 'raise') – What to do then an error happend. When ‘raise’ is given, raise an exception. When ‘warn’ is given, produce a (Python) warning. K – the optimal rate matrix (N,N) ndarray

Notes

In this implementation the algorithm of Crommelin and Vanden-Eijnden (CVE) is initialized with the pseudo-generator estimate. The algorithm of Kalbfleisch and Lawless (KL) is initialized using the CVE result.

Example

>>> import numpy as np
>>> from msmtools.estimation import rate_matrix
>>> C = np.array([[100,1],[50,50]])
>>> rate_matrix(C)
array([[-0.01384753,  0.01384753],
[ 0.69930032, -0.69930032]])


References

  D. Crommelin and E. Vanden-Eijnden. Data-based inference of generators for markov jump processes using convex optimization. Multiscale. Model. Sim., 7(4):1751-1778, 2009.
  J. D. Kalbfleisch and J. F. Lawless. The analysis of panel data under a markov assumption. J. Am. Stat. Assoc., 80(392):863-871, 1985.
  E. B. Davies. Embeddable Markov Matrices. Electron. J. Probab. 15:1474, 2010.