/
test_bootstrap.py
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/
test_bootstrap.py
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from ..util import BaseCase
from . import fixtures as pkg
from pygsti import algorithms as alg, construction as pc
from pygsti.objects import DataSet
from pygsti.modelpacks.legacy import std1Q_XYI as std
from pygsti.drivers import bootstrap as bs
class BootstrapBase(BaseCase):
@classmethod
def setUpClass(cls):
cls.opLabels = pkg.opLabels
cls.fiducials = pkg.fiducials
cls.germs = pkg.germs
cls.ds = pkg.dataset
tp_target = std.target_model()
tp_target.set_all_parameterizations("TP")
cls.mdl = alg.run_lgst(
cls.ds, cls.fiducials, cls.fiducials, target_model=tp_target, svd_truncate_to=4, verbosity=0
)
def setUp(self):
self.ds = self.ds.copy()
self.mdl = self.mdl.copy()
class BootstrapDatasetTester(BootstrapBase):
def test_make_bootstrap_dataset_parametric(self):
bootds_p = bs.create_bootstrap_dataset(self.ds, 'parametric', self.mdl, seed=1234)
# TODO assert correctness
def test_make_bootstrap_dataset_nonparametric(self):
bootds_np = bs.create_bootstrap_dataset(self.ds, 'nonparametric', seed=1234)
# TODO assert correctness
def test_make_bootstrap_dataset_raises_on_bad_generation_method(self):
with self.assertRaises(ValueError):
bs.create_bootstrap_dataset(self.ds, 'foobar', seed=1)
def test_make_bootstrap_dataset_raises_on_no_parametric_model(self):
with self.assertRaises(ValueError):
bs.create_bootstrap_dataset(self.ds, 'parametric', seed=1)
def test_make_bootstrap_dataset_raises_on_nonparametric_with_model(self):
with self.assertRaises(ValueError):
bs.create_bootstrap_dataset(self.ds, 'nonparametric', self.mdl, seed=1)
class BootstrapModelTester(BootstrapBase):
def setUp(self):
super(BootstrapModelTester, self).setUp()
self.maxLengths = [0] # just do LGST strings to make this fast...
def test_make_bootstrap_models_parametric(self):
# TODO optimize
bootgs_p = bs.create_bootstrap_models(
2, self.ds, 'parametric', self.fiducials, self.fiducials,
self.germs, self.maxLengths, input_model=self.mdl,
return_data=False
)
# TODO assert correctness
def test_make_bootstrap_models_with_list(self):
# TODO optimize
custom_strs = pc.create_lsgst_circuit_lists(
self.mdl, self.fiducials, self.fiducials, self.germs, [1]
)
bootgs_p_custom = bs.create_bootstrap_models(
2, self.ds, 'parametric', None, None, None, None,
lsgst_lists=custom_strs, input_model=self.mdl,
return_data=False
)
# TODO assert correctness
def test_make_bootstrap_models_nonparametric(self):
# TODO optimize
bootgs_np, bootds_np2 = bs.create_bootstrap_models(
2, self.ds, 'nonparametric', self.fiducials, self.fiducials,
self.germs, self.maxLengths, target_model=self.mdl,
return_data=True
)
# TODO assert correctness
def test_make_bootstrap_models_raises_on_no_model(self):
with self.assertRaises(ValueError):
bs.create_bootstrap_models(
2, self.ds, 'parametric', self.fiducials, self.fiducials,
self.germs, self.maxLengths, return_data=False
)
def test_make_bootstrap_models_raises_on_conflicting_model_input(self):
with self.assertRaises(ValueError):
bs.create_bootstrap_models(
2, self.ds, 'parametric', self.fiducials, self.fiducials,
self.germs, self.maxLengths, input_model=self.mdl, target_model=self.mdl,
return_data=False
)
class BootstrapUtilityTester(BootstrapBase):
@classmethod
def setUpClass(cls):
super(BootstrapUtilityTester, cls).setUpClass()
maxLengths = [0]
cls.bootgs_p = bs.create_bootstrap_models(
2, cls.ds, 'parametric', cls.fiducials, cls.fiducials,
cls.germs, maxLengths, input_model=cls.mdl,
return_data=False
)
def setUp(self):
super(BootstrapUtilityTester, self).setUp()
self.bootgs_p = self.bootgs_p[:] # python2-compatible copy
def test_gauge_optimize_model_list(self):
bs.gauge_optimize_models(
self.bootgs_p, std.target_model(), gate_metric='frobenius',
spam_metric='frobenius', plot=False
)
# TODO assert correctness
def test_gauge_optimize_model_list_with_plot(self):
with self.assertRaises(NotImplementedError):
bs.gauge_optimize_models(
self.bootgs_p, std.target_model(), gate_metric='frobenius',
spam_metric='frobenius', plot=True)
def test_bootstrap_utilities(self):
#Test utility functions -- just make sure they run for now...
def gsFn(mdl):
return mdl.dim
tp_target = std.target_model()
tp_target.set_all_parameterizations("TP")
bs._model_stdev(gsFn, self.bootgs_p)
bs._model_mean(gsFn, self.bootgs_p)
bs._to_mean_model(self.bootgs_p, tp_target)
bs._to_std_model(self.bootgs_p, tp_target)
bs._to_rms_model(self.bootgs_p, tp_target)
# TODO assert correctness