pygsti.protocols.estimate
Defines the Estimate class.
Module Contents
Classes
A class encapsulating the Model objects related to a single GST estimate up-to-gauge freedoms. |
Attributes
- pygsti.protocols.estimate.CRFkey
- class pygsti.protocols.estimate.Estimate(parent, models=None, parameters=None, extra_parameters=None)
Bases:
pygsti.baseobjs.mongoserializable.MongoSerializable
A class encapsulating the Model objects related to a single GST estimate up-to-gauge freedoms.
Thus, this class holds the “iteration” Model`s leading up to a final `Model, and then different gauge optimizations of the final set.
Parameters
- parentResults
The parent Results object containing the dataset and circuit structure used for this Estimate.
- modelsdict, optional
A dictionary of models to included in this estimate
- parametersdict, optional
A dictionary of parameters associated with how these models were obtained.
Initialize an empty Estimate object.
Parameters
- parentResults
The parent Results object containing the dataset and circuit structure used for this Estimate.
- modelsdict, optional
A dictionary of models to included in this estimate
- parametersdict, optional
A dictionary of parameters associated with how these models were obtained.
- property parameters
- property goparameters
- collection_name = "'pygsti_estimates'"
- classmethod from_dir(dirname, quick_load=False)
Initialize a new Protocol object from dirname.
- quick_loadbool, optional
Setting this to True skips the loading of components that may take a long time to load.
Parameters
- dirnamestr
The directory name.
- quick_loadbool, optional
Setting this to True skips the loading of components that may take a long time to load.
Returns
Protocol
- classmethod create_gst_estimate(parent, target_model=None, seed_model=None, models_by_iter=None, parameters=None)
Initialize an empty Estimate object.
Parameters
- parentResults
The parent Results object containing the dataset and circuit structure used for this Estimate.
- target_modelModel
The target model used when optimizing the objective.
- seed_modelModel
The initial model used to seed the iterative part of the objective optimization. Typically this is obtained via LGST.
- models_by_iterlist of Models
The estimated model at each GST iteration. Typically these are the estimated models before any gauge optimization is performed.
- parametersdict
A dictionary of parameters associated with how these models were obtained.
Returns
Estimate
- write(dirname)
Write this protocol to a directory.
Parameters
- dirnamestr
The directory name to write. This directory will be created if needed, and the files in an existing directory will be overwritten.
Returns
None
- classmethod remove_from_mongodb(mongodb_collection, doc_id, session=None)
Remove an Estimate from a MongoDB database.
Returns
- bool
True if the specified experiment design was removed, False if it didn’t exist.
- retrieve_start_model(goparams)
Returns the starting model for the gauge optimization given by goparams.
This has a particular (and perhaps singular) use for deciding whether the gauge-optimized model for one estimate can be simply copied to another estimate, without actually re-gauge-optimizing.
Parameters
- goparamsdict or list
A dictionary of gauge-optimization parameters, just as in
add_gaugeoptimized()
.
Returns
Model
- add_gaugeoptimized(goparams, model=None, label=None, comm=None, verbosity=None)
Adds a gauge-optimized Model (computing it if needed) to this object.
Parameters
- goparamsdict or list
A dictionary of gauge-optimization parameters, typically arguments to
gaugeopt_to_target()
, specifying how the gauge optimization was (or should be) performed. When model is None (and this function computes the model internally) the keys and values of this dictionary must correspond to allowed arguments ofgaugeopt_to_target()
. By default,gaugeopt_to_target()
’s first two arguments, the Model to optimize and the target, are taken to be self.models[‘final iteration estimate’] and self.models[‘target’]. This argument can also be a list of such parameter dictionaries, which specifies a multi-stage gauge- optimization whereby the output of one stage is the input of the next.- modelModel, optional
The gauge-optimized model to store. If None, then this model is computed by calling
gaugeopt_to_target()
with the contents of goparams as arguments as described above.- labelstr, optional
A label for this gauge-optimized model, used as the key in this object’s models and goparameters member dictionaries. If None, then the next available “go<X>”, where <X> is a non-negative integer, is used as the label.
- commmpi4py.MPI.Comm, optional
A default MPI communicator to use when one is not specified as the ‘comm’ element of/within goparams.
- verbosityint, optional
An integer specifying the level of detail printed to stdout during the calculations performed in this function. If not None, this value will override any verbosity values set within goparams.
Returns
None
- add_confidence_region_factory(model_label='final iteration estimate', circuits_label='final')
Creates a new confidence region factory.
An instance of
ConfidenceRegionFactory
serves to create confidence intervals and regions in reports and elsewhere. This function creates such a factory, which is specific to a given Model (given by this object’s .models[model_label] ) and circuit list (given by the parent Results’s .circuit_lists[gastrings_label] list).Parameters
- model_labelstr, optional
The label of a Model held within this Estimate.
- circuits_labelstr, optional
The label of a circuit list within this estimate’s parent Results object.
Returns
- ConfidenceRegionFactory
The newly created factory (also cached internally) and accessible via the
create_confidence_region_factory()
method.
- has_confidence_region_factory(model_label='final iteration estimate', circuits_label='final')
Checks whether a confidence region factory for the given model and circuit list labels exists.
Parameters
- model_labelstr, optional
The label of a Model held within this Estimate.
- circuits_labelstr, optional
The label of a circuit list within this estimate’s parent Results object.
Returns
bool
- create_confidence_region_factory(model_label='final iteration estimate', circuits_label='final', create_if_needed=False)
Retrieves a confidence region factory for the given model and circuit list labels.
For more information about confidence region factories, see
add_confidence_region_factory()
.Parameters
- model_labelstr, optional
The label of a Model held within this Estimate.
- circuits_labelstr, optional
The label of a circuit list within this estimate’s parent Results object.
- create_if_neededbool, optional
If True, a new confidence region factory will be created if none exists. Otherwise a KeyError is raised when the requested factory doesn’t exist.
Returns
ConfidenceRegionFactory
- gauge_propagate_confidence_region_factory(to_model_label, from_model_label='final iteration estimate', circuits_label='final', eps=0.001, verbosity=0)
Propagates a confidence region among gauge-equivalent models.
More specifically, this function propagates an existing “reference” confidence region for a Model “G0” to a new confidence region for a gauge-equivalent model “G1”.
When successful, a new confidence region factory is created for the .models[to_model_label] Model and circuits_label gate string list from the existing factory for .models[from_model_label].
Parameters
- to_model_labelstr
The key into this Estimate object’s models and goparameters dictionaries that identifies the final gauge-optimized result to create a factory for. This gauge optimization must have begun at “from” reference model, i.e., models[from_model_label] must equal (by frobeinus distance) goparameters[to_model_label][‘model’].
- from_model_labelstr, optional
The key into this Estimate object’s models dictionary that identifies the reference model.
- circuits_labelstr, optional
The key of the circuit list (within the parent Results’s .circuit_lists dictionary) that identifies the circuit list used by the old (&new) confidence region factories.
- epsfloat, optional
A small offset used for constructing finite-difference derivatives. Usually the default value is fine.
- verbosityint, optional
A non-negative integer indicating the amount of detail to print to stdout.
Returns
- ConfidenceRegionFactory
Note: this region is also stored internally and as such the return value of this function can often be ignored.
- create_effective_dataset(return_submxs=False)
Generate a DataSet containing the effective counts as dictated by the “weights” parameter.
An estimate’s self.parameters[‘weights’] value specifies a dictionary of circuits weights, which modify (typically reduce) the counts given in its (parent’s) data set.
This function rescales the actual data contained in this Estimate’s parent
ModelEstimteResults
object according to the estimate’s “weights” parameter. The scaled data set is returned, along with (optionall) a list-of-lists of matrices containing the scaling values which can be easily plotted via a ColorBoxPlot.Parameters
- return_submxsboolean
If true, also return a list-of-lists of matrices containing the scaling values, as described above.
Returns
- dsDataSet
The “effective” (scaled) data set.
- subMxslist-of-lists
Only returned if return_submxs == True. Contains the scale values (see above).
- final_mdc_store(resource_alloc=None, array_types=('e', 'ep'))
The final (not intermediate) model-dataset-circuit storage object (MDC store) for this estimate.
This object is created and cached as needed, and combined the final model, data set, and circuit list for this estimate.
Parameters
- resource_allocResourceAllocation
The resource allocation object used to create the MDC store. This can just be left as None unless multiple processors are being utilized. Note that this argument is only used when a MDC store needs to be created – if this estimate has already created one then this argument is ignored.
- array_typestuple
A tuple of array types passed to the MDC store constructor (if a new MDC store needs to be created). These affect how memory is allocated within the MDC store object and can enable (or disable) the use of certain MDC store functionality later on (e.g. the use of Jacobian or Hessian quantities).
Returns
ModelDatasetCircuitsStore
- final_objective_fn(resource_alloc=None)
The final (not intermediate) objective function object for this estimate.
This object is created and cached as needed, and is the evaluated (and sometimes optimized) objective function associated with this estimate. Often this is a log-likelihood or chi-squared function, or a close variant.
Parameters
- resource_allocResourceAllocation
The resource allocation object used to create the MDC store underlying the objective function. This can just be left as None unless multiple processors are being utilized. Note that this argument is only used when an underlying MDC store needs to be created – if this estimate has already created a MDC store then this argument is ignored.
Returns
MDCObjectiveFunction
- final_objective_fn_cache(resource_alloc=None)
The final (not intermediate) serializable (“cached”) objective function object for this estimate.
This is an explicitly serializable version of the final objective function, useful because is often doesn’t need be constructed. To become serializable, however, the objective function is stripped of any MPI comm or multi-processor information (since this may be different between loading and saving). This makes the cached objective function convenient for fast calls/usages of the objective function.
Parameters
- resource_allocResourceAllocation
The resource allocation object used to create the MDC store underlying the objective function. This can just be left as None unless multiple processors are being utilized - and in this case the cached objective function doesn’t even benefit from these processors (but calls to
final_objective_fn()
will return an objective function setup for multiple processors). Note that this argument is only used when there is no existing cached objective function and an underlying MDC store needs to be created.
Returns
CachedObjectiveFunction
- misfit_sigma(resource_alloc=None)
Returns the number of standard deviations (sigma) of model violation.
Parameters
- resource_allocResourceAllocation, optional
What resources are available for this computation.
Returns
float
- view(gaugeopt_keys, parent=None)
Creates a shallow copy of this Results object containing only the given gauge-optimization keys.
Parameters
- gaugeopt_keysstr or list, optional
Either a single string-value gauge-optimization key or a list of such keys. If None, then all gauge-optimization keys are retained.
- parentResults, optional
The parent Results object of the view. If None, then the current Estimate’s parent is used.
Returns
Estimate