def cktest(self,
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 : :class:`ChapmanKolmogorovValidator <pyemma.msm.ChapmanKolmogorovValidator>`
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)
"""
from pyemma.msm.estimators import ChapmanKolmogorovValidator
ck = ChapmanKolmogorovValidator(self,
self,
_np.eye(self.nstates),
mlags=mlags,
conf=conf,
err_est=err_est,
n_jobs=n_jobs,
show_progress=show_progress)
ck.estimate(self._dtrajs_full)
return ck
def cktest(self,
nsets,
memberships=None,
mlags=10,
conf=0.95,
err_est=False,
show_progress=True):
""" Conducts a Chapman-Kolmogorow test.
Parameters
----------
nsets : int
number of sets to test on
memberships : ndarray(nstates, nsets), optional
optional state memberships. By default (None) will conduct a cktest
on PCCA (metastable) sets.
mlags : int or int-array, optional
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
confidence interval
err_est : bool, optional
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.
show_progress : bool, optional
Show progressbars for calculation?
References
----------
This test was suggested in [1]_ and described in detail in [2]_.
.. [1] F. Noe, Ch. Schuette, E. Vanden-Eijnden, L. Reich and
T. Weikl: Constructing the Full Ensemble of Folding Pathways
from Short Off-Equilibrium Simulations.
Proc. Natl. Acad. Sci. USA, 106, 19011-19016 (2009)
.. [2] 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
"""
from pyemma.msm.estimators import ChapmanKolmogorovValidator
if memberships is None:
self.pcca(nsets)
memberships = self.metastable_memberships
ck = ChapmanKolmogorovValidator(self,
self,
memberships,
mlags=mlags,
conf=conf,
err_est=err_est)
ck.estimate(self._dtrajs_full)
return ck
