/
test_estimate.py
81 lines (64 loc) · 2.77 KB
/
test_estimate.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
import pickle
from ..util import BaseCase, mock
from . import fixtures as pkg
from pygsti.construction import std1Q_XYI as std
from pygsti.objects.results import Results
from pygsti.objects import estimate
class EstimateBase(object):
@classmethod
def setUpClass(cls):
cls.model = pkg.mdl_lsgst_go
cls.maxLengthList = pkg.maxLengthList
cls.res = Results()
cls.res.init_dataset(pkg.dataset)
cls.res.init_circuits(pkg.lsgstStructs)
def setUp(self):
self.model = self.model.copy()
self.res = self.res.copy()
def test_get_effective_dataset(self):
# Get effective estimate dataset
effds = self.est.get_effective_dataset()
effds, subMxs = self.est.get_effective_dataset(return_subMxs=True)
# TODO assert correctness
def test_view(self):
#Estimate views
est_view = self.est.view(None)
est_view = self.est.view(['test'])
# TODO assert correctness
def test_to_string(self):
#Estimate & results render as str
s = str(self.est)
# TODO assert correctness
def test_pickle(self):
s = pickle.dumps(self.est)
est_pickled = pickle.loads(s)
# TODO assert correctness
class ResultsEstimateTester(EstimateBase, BaseCase):
def setUp(self):
super(ResultsEstimateTester, self).setUp()
self.res.add_estimate(
std.target_model(), std.target_model(),
[self.model] * len(self.maxLengthList),
parameters={'objective': 'logl'},
estimate_key="default"
)
self.est = self.res.estimates['default']
def test_add_gaugeoptimized(self):
# TODO optimize
goparams = {'itemWeights': {'gates': 1.0, 'spam': 0.1},
'method': 'BFGS'} # method so we don't need a legit comm
self.est.add_gaugeoptimized(goparams, label="test", comm=None, verbosity=None)
# TODO assert correctness
class EmptyEstimateTester(EstimateBase, BaseCase):
def setUp(self):
super(EmptyEstimateTester, self).setUp()
self.est = estimate.Estimate(self.res)
def test_add_gaugeoptimized_raises_on_no_model(self):
with self.assertRaises(ValueError):
goparams = {'itemWeights': {'gates': 1.0, 'spam': 0.1}, 'targetModel': self.model}
self.est.add_gaugeoptimized(goparams, label="test", comm=None, verbosity=None) # goparams must have 'model'
def test_add_gaugeoptimized_raises_on_no_target_model(self):
with self.assertRaises(ValueError):
goparams = {'itemWeights': {'gates': 1.0, 'spam': 0.1}, 'model': self.model}
self.est.add_gaugeoptimized(goparams, label="test", comm=None,
verbosity=None) # goparams must have 'targetModel'