pyemma.msm.ChapmanKolmogorovValidator

class pyemma.msm.ChapmanKolmogorovValidator(model, estimator, memberships, mlags=None, conf=0.95, err_est=False, n_jobs=1, show_progress=True)
__init__(model, estimator, memberships, mlags=None, conf=0.95, err_est=False, n_jobs=1, show_progress=True)
Parameters:memberships (ndarray(n, m)) – Set memberships to calculate set probabilities. n must be equal to the number of active states in model. m is the number of sets. memberships must be a row-stochastic matrix (the rows must sum up to 1).

Methods

__init__(model, estimator, memberships[, …])
param memberships:
 Set memberships to calculate set probabilities. n must be equal to
estimate(X, **params) Estimates the model given the data X
fit(X[, y]) Estimates parameters - for compatibility with sklearn.
get_params([deep]) Get parameters for this estimator.
load(file_name[, model_name]) loads a previously saved object of this class from a file.
save(file_name[, model_name, overwrite, …]) saves the current state of this object to given file and name.
set_params(**params) Set the parameters of this estimator.
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
estimates

Returns estimates at different lagtimes

Returns:Y – each row contains the n observables computed at one of the T lag t imes.
Return type:ndarray(T, n)
estimates_conf

Returns the confidence intervals of the estimates at different lagtimes (if available).

If not available, returns None.

Returns:
  • L (ndarray(T, n)) – each row contains the lower confidence bound of n observables computed at one of the T lag times.
  • R (ndarray(T, n)) – each row contains the upper confidence bound of n observables computed at one of the T lag times.
fit(X, y=None)

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
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
load(file_name, model_name='default')

loads a previously saved object of this class from a file.

Parameters:
  • file_name (str or file like object (has to provide read method)) – The file like object tried to be read for a serialized object.
  • model_name (str, default='default') – if multiple models are contained in the file, these can be accessed by their name. Use func:pyemma.list_models to get a representation of all stored models.
Returns:

obj
Return type:

the de-serialized object
logger

The logger for this class instance

model

The model estimated by this Estimator

name

The name of this instance

predictions

Returns tested model predictions at different lagtimes

Returns:Y – each row contains the n observables predicted at one of the T lag times by the tested model.
Return type:ndarray(T, n)
predictions_conf

Returns the confidence intervals of the estimates at different lagtimes (if available)

If not available, returns None.

Returns:
  • L (ndarray(T, n)) – each row contains the lower confidence bound of n observables computed at one of the T lag times.
  • R (ndarray(T, n)) – each row contains the upper confidence bound of n observables computed at one of the T lag times.
save(file_name, model_name='default', overwrite=False, save_streaming_chain=False)

saves the current state of this object to given file and name.

Parameters:
  • file_name (str) – path to desired output file
  • model_name (str, default='default') – creates a group named ‘model_name’ in the given file, which will contain all of the data. If the name already exists, and overwrite is False (default) will raise a RuntimeError.
  • overwrite (bool, default=False) – Should overwrite existing model names?
  • save_streaming_chain (boolean, default=False) – if True, the data_producer(s) of this object will also be saved in the given file.

Examples

>>> import pyemma, numpy as np
>>> from pyemma.util.contexts import named_temporary_file
>>> m = pyemma.msm.MSM(P=np.array([[0.1, 0.9], [0.9, 0.1]]))

>>> with named_temporary_file() as file: 
...    m.save(file, 'simple') 
...    inst_restored = pyemma.load(file, 'simple') 
>>> np.testing.assert_equal(m.P, inst_restored.P)
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

show_progress

whether to show the progress of heavy calculations on this object.