def test_long_sequence_gst_raises_on_bad_profile_options(self):
#check invalid profile options
with self.assertRaises(ValueError):
ls.run_long_sequence_gst(self.ds,
self.model,
self.fiducials,
self.fiducials,
self.germs,
self.maxLens,
advanced_options={'profile': 3})
def test_long_sequence_gst_raises_on_bad_badfit_options(self):
with self.assertRaises(ValueError):
ls.run_long_sequence_gst(self.ds,
self.model,
self.fiducials,
self.fiducials,
self.germs,
self.maxLens,
advanced_options={
'bad_fit_threshold': -100,
'on_bad_fit': ["foobar"]
})
def test_long_sequence_gst(self):
result = ls.run_long_sequence_gst(self.ds,
self.model,
self.fiducials,
self.fiducials,
self.germs,
self.maxLens,
advanced_options=self.options)
def test_long_sequence_gst_chi2(self):
self.options.update(objective='chi2')
result = ls.run_long_sequence_gst(self.ds,
self.model,
self.fiducials,
self.fiducials,
self.germs,
self.maxLens,
advanced_options=self.options)
def test_long_sequence_gst_raises_on_bad_advanced_options(self):
with self.assertRaises(ValueError):
ls.run_long_sequence_gst(self.ds,
self.model,
self.fiducials,
None,
self.germs,
self.maxLens,
advanced_options={'objective': "FooBar"
}) # bad objective
with self.assertRaises(ValueError):
ls.run_long_sequence_gst(
self.ds,
self.model,
self.fiducials,
None,
self.germs,
self.maxLens,
advanced_options={'starting_point':
"FooBar"}) # bad starting point
def test_long_sequence_gst_advanced_options(self):
# TODO what exactly is being tested?
self.options.update({
#'starting point': self.model, #this is deprecated now - need to use protocol objects
'depolarize_start': 0.05,
'cptp_penalty_factor': 1.0
})
result = ls.run_long_sequence_gst(self.ds,
self.model,
self.fiducials,
None,
self.germs,
self.maxLens,
advanced_options=self.options)
def test_long_sequence_gst_with_file_args(self, ds_path, model_path,
fiducial_path, germ_path):
io.write_dataset(ds_path, self.ds, self.lsgstStrings[-1])
io.write_model(self.model, model_path)
io.write_circuit_list(fiducial_path, self.fiducials)
io.write_circuit_list(germ_path, self.germs)
self.options.update(
randomize_start=1e-6,
profile=2,
)
result = ls.run_long_sequence_gst(ds_path,
model_path,
fiducial_path,
fiducial_path,
germ_path,
self.maxLens,
advanced_options=self.options,
verbosity=10)
def create_bootstrap_models(num_models,
input_data_set,
generation_method,
fiducial_prep,
fiducial_measure,
germs,
max_lengths,
input_model=None,
target_model=None,
start_seed=0,
outcome_labels=None,
lsgst_lists=None,
return_data=False,
verbosity=2):
"""
Creates a series of "bootstrapped" Models.
Models are created from a single DataSet (and possibly Model) and are
typically used for generating bootstrapped error bars. The resulting Models
are obtained by performing MLGST on data generated by repeatedly calling
:function:`create_bootstrap_dataset` with consecutive integer seed values.
Parameters
----------
num_models : int
The number of models to create.
input_data_set : DataSet
The data set to use for generating the "bootstrapped" data set.
generation_method : { 'nonparametric', 'parametric' }
The type of data to generate. 'parametric' generates DataSets
with the same circuits and sample counts as input_data_set but
using the probabilities in input_model (which must be provided).
'nonparametric' generates DataSets with the same circuits
and sample counts as input_data_set using the count frequencies of
input_data_set as probabilities.
fiducial_prep : list of Circuits
The state preparation fiducial circuits used by MLGST.
fiducial_measure : list of Circuits
The measurement fiducial circuits used by MLGST.
germs : list of Circuits
The germ circuits used by MLGST.
max_lengths : list of ints
List of integers, one per MLGST iteration, which set truncation lengths
for repeated germ strings. The list of circuits for the i-th LSGST
iteration includes the repeated germs truncated to the L-values *up to*
and including the i-th one.
input_model : Model, optional
The model used to compute the probabilities for circuits when
generation_method is set to 'parametric'. If 'nonparametric' is selected,
this argument must be set to None (the default).
target_model : Model, optional
Mandatory model to use for as the target model for MLGST when
generation_method is set to 'nonparametric'. When 'parametric'
is selected, this should be the ideal version of `input_model`.
start_seed : int, optional
The initial seed value for numpy's random number generator when
generating data sets. For each succesive dataset (and model)
that are generated, the seed is incremented by one.
outcome_labels : list, optional
The list of Outcome labels to include in the output dataset. If None
are specified, defaults to the effect labels of `input_data_set`.
lsgst_lists : list of circuit lists, optional
Provides explicit list of circuit lists to be used in analysis;
to be given if the dataset uses "incomplete" or "reduced" sets of
circuit. Default is None.
return_data : bool
Whether generated data sets should be returned in addition to
models.
verbosity : int
Level of detail printed to stdout.
Returns
-------
models : list
The list of generated Model objects.
data : list
The list of generated DataSet objects, only returned when
return_data == True.
"""
if max_lengths is None:
print("No max_lengths value specified; using [0,1,2,4,...,1024]")
max_lengths = [0] + [2**k for k in range(10)]
if (input_model is None and target_model is None):
raise ValueError("Must supply either input_model or target_model!")
#if (input_model is not None and target_model is not None):
# raise ValueError("Cannot supply both input_model and target_model!")
if generation_method == 'parametric' and target_model is None:
target_model = input_model
datasetList = []
print("Creating DataSets: ")
for run in range(num_models):
print("%d " % run, end='')
datasetList.append(
create_bootstrap_dataset(input_data_set, generation_method,
input_model, start_seed + run,
outcome_labels))
modelList = []
print("Creating Models: ")
for run in range(num_models):
print("Running MLGST Iteration %d " % run)
if lsgst_lists is not None:
results = _longseq.run_long_sequence_gst_base(datasetList[run],
target_model,
lsgst_lists,
verbosity=verbosity)
else:
results = _longseq.run_long_sequence_gst(datasetList[run],
target_model,
fiducial_prep,
fiducial_measure,
germs,
max_lengths,
verbosity=verbosity)
modelList.append(
results.estimates.get(
'default',
next(iter(results.estimates.values()))).models['go0'])
if not return_data:
return modelList
else:
return modelList, datasetList
