pyemma.coordinates.transform.VAMPChapmanKolmogorovValidator

class pyemma.coordinates.transform.VAMPChapmanKolmogorovValidator(*args, **kwargs)

__init__(test_model, test_estimator, observables, statistics, observables_mean_free, statistics_mean_free, mlags=10, n_jobs=None, show_progress=True)

Note

It is recommended that you create this object by calling the cktest method of a VAMP object created with vamp.

Parameters

• test_model (Model) – Model with the smallest lag time. Is used to make predictions for larger lag times.

• test_estimator (Estimator) – Parametrized Estimator that has produced the model. Is used as a prototype for estimating models at higher lag times.

• observables (np.ndarray((input_dimension, n_observables))) – Coefficients that express one or multiple observables in the basis of the input features.

• statistics (np.ndarray((input_dimension, n_statistics))) – Coefficients that express one or multiple statistics in the basis of the input features.

• observables_mean_free (bool, default=False) – If true, coefficients in observables refer to the input features with feature means removed. If false, coefficients in observables refer to the unmodified input features.

• statistics_mean_free (bool, default=False) – If true, coefficients in statistics refer to the input features with feature means removed. If false, coefficients in statistics refer to the unmodified input features.

• 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). Note that you need to be able to do a model prediction for each of these lag time multiples, e.g. the value 0 only make sense if model.expectation(lag_multiple=0) will work.

• n_jobs (int, default=None) – how many jobs to use during calculation

• show_progress (bool, default=True) – Show progressbars for calculation?

Notes

The object can be plotted with plot_cktest with the option y01=False.

Methods

_Loggable__create_logger()
_ProgressReporterMixin__check_stage_registered(stage)
_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(test_model, test_estimator, …[, …])
__init_subclass__(*args, **kwargs)	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__my_getstate__()
__my_setstate__(state)
__ne__(value, /)	Return self!=value.
__new__(cls, *args, **kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_check_estimated()
_cleanup_logger(logger_id, logger_name)
_compute_observables(model, estimator[, mlag])	Compute observables for given model
_compute_observables_conf(model, estimator)	Compute confidence interval for observables for given model
_estimate(data)
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_get_param_names()	Get parameter names for the estimator
_get_private_field(cls, name[, default])
_get_serialize_fields(cls)
_get_state_of_serializeable_fields(klass, state)	:return a dictionary {k:v} for k in self.serialize_fields and v=getattr(self, k)
_get_version(cls[, require])
_get_version_for_class_from_state(state, klass)	retrieves the version of the current klass from the state mapping from old locations to new ones.
_logger_is_active(level)	@param level: int log level (debug=10, info=20, warn=30, error=40, critical=50)
_progress_context([stage])	param stage
_progress_force_finish([stage, description])	forcefully finish the progress for given stage
_progress_register(amount_of_work[, …])	Registers a progress which can be reported/displayed via a progress bar.
_progress_set_description(stage, description)	set description of an already existing progress
_progress_update(numerator_increment[, …])	Updates the progress.
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
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 PyEMMA object from disk.
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

_Estimator__serialize_fields
_LaggedModelValidator__serialize_fields
_LaggedModelValidator__serialize_modifications_map
_LaggedModelValidator__serialize_version
_Loggable__ids
_Loggable__refs
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
_VAMPChapmanKolmogorovValidator__serialize_fields
_VAMPChapmanKolmogorovValidator__serialize_version
__dict__
__doc__
__module__
__weakref__	list of weak references to the object (if defined)
_estimated
_loglevel_CRITICAL
_loglevel_DEBUG
_loglevel_ERROR
_loglevel_INFO
_loglevel_WARN
_pg_threshold
_prog_rep_callbacks
_prog_rep_descriptions
_prog_rep_progressbars
_progress_num_registered
_progress_registered_stages
_save_data_producer
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
model	The model estimated by this Estimator
n_jobs	Returns number of jobs/threads to use during assignment of data.
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.
statistics