pyemma.msm.ChapmanKolmogorovValidator¶

class pyemma.msm.ChapmanKolmogorovValidator(test_model, test_estimator, memberships, mlags=None, conf=0.95, err_est=False, n_jobs=1, show_progress=True)¶

__init__(test_model, test_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__(test_model, test_estimator, memberships)

param memberships:
	Set memberships to calculate set probabilities.

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.

Attributes

`estimates`	Returns estimates at different lagtimes
`estimates_conf`	Returns the confidence intervals of the estimates at different lagtimes (if available).
`lagtimes`
`logger`	The logger for this class instance
`memberships`
`model`	The model estimated by this Estimator
`name`	The name of this instance
`predictions`	Returns tested model predictions at different lagtimes
`predictions_conf`	Returns the confidence intervals of the estimates at different lagtimes (if available)
`show_progress`	whether to show the progress of heavy calculations on this object.
`test_estimator`
`test_model`

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

classmethod 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.