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testHessian.py
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testHessian.py
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import unittest
import warnings
import pygsti
from pygsti.construction import std1Q_XYI as stdxyi
from pygsti.construction import std1Q_XY as stdxy
from pygsti.objects import modelfunction as gsf
from pygsti.objects.mapforwardsim import MapForwardSimulator
from pygsti.objects import Label as L
import numpy as np
import sys, os
import pickle
from ..testutils import BaseTestCase, compare_files, temp_files
class TestHessianMethods(BaseTestCase):
def setUp(self):
super(TestHessianMethods, self).setUp()
self.model = pygsti.io.load_model(compare_files + "/analysis.model")
self.ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset%s" % self.versionsuffix)
fiducials = stdxyi.fiducials
germs = stdxyi.germs
opLabels = list(self.model.operations.keys()) # also == std.gates
self.maxLengthList = [1,2]
self.gss = pygsti.construction.make_lsgst_structs(opLabels, fiducials, fiducials, germs, self.maxLengthList)
def test_parameter_counting(self):
#XY Model: SPAM=True
n = stdxy.target_model().num_params()
self.assertEqual(n,44) # 2*16 + 3*4 = 44
n = stdxy.target_model().num_nongauge_params()
self.assertEqual(n,28) # full 16 gauge params
#XY Model: SPAM=False
tst = stdxy.target_model()
del tst.preps['rho0']
del tst.povms['Mdefault']
n = tst.num_params()
self.assertEqual(n,32) # 2*16 = 32
n = tst.num_nongauge_params()
self.assertEqual(n,18) # gates are all unital & TP => only 14 gauge params (2 casimirs)
#XYI Model: SPAM=True
n = stdxyi.target_model().num_params()
self.assertEqual(n,60) # 3*16 + 3*4 = 60
n = stdxyi.target_model().num_nongauge_params()
self.assertEqual(n,44) # full 16 gauge params: SPAM gate + 3 others
#XYI Model: SPAM=False
tst = stdxyi.target_model()
del tst.preps['rho0']
del tst.povms['Mdefault']
n = tst.num_params()
self.assertEqual(n,48) # 3*16 = 48
n = tst.num_nongauge_params()
self.assertEqual(n,34) # gates are all unital & TP => only 14 gauge params (2 casimirs)
#XYI Model: SP0=False
tst = stdxyi.target_model()
tst.preps['rho0'] = pygsti.obj.TPSPAMVec(tst.preps['rho0'])
n = tst.num_params()
self.assertEqual(n,59) # 3*16 + 2*4 + 3 = 59
n = tst.num_nongauge_params()
self.assertEqual(n,44) # 15 gauge params (minus one b/c can't change rho?)
#XYI Model: G0=SP0=False
tst.operations['Gi'] = pygsti.obj.TPDenseOp(tst.operations['Gi'])
tst.operations['Gx'] = pygsti.obj.TPDenseOp(tst.operations['Gx'])
tst.operations['Gy'] = pygsti.obj.TPDenseOp(tst.operations['Gy'])
n = tst.num_params()
self.assertEqual(n,47) # 3*12 + 2*4 + 3 = 47
n = tst.num_nongauge_params()
self.assertEqual(n,35) # full 12 gauge params of single 4x3 gate
def test_hessian_projection(self):
chi2, chi2Grad, chi2Hessian = pygsti.chi2(self.model, self.ds,
returnGradient=True,
returnHessian=True)
proj_non_gauge = self.model.get_nongauge_projector()
projectedHessian = np.dot(proj_non_gauge,
np.dot(chi2Hessian, proj_non_gauge))
print(self.model.num_params())
print(proj_non_gauge.shape)
self.assertEqual( projectedHessian.shape, (60,60) )
#print("Evals = ")
#print("\n".join( [ "%d: %g" % (i,ev) for i,ev in enumerate(np.linalg.eigvals(projectedHessian))] ))
self.assertEqual( np.linalg.matrix_rank(proj_non_gauge), 44)
self.assertEqual( np.linalg.matrix_rank(projectedHessian), 44)
eigvals = np.sort(abs(np.linalg.eigvals(projectedHessian)))
print("eigvals = ",eigvals)
eigvals_chk = np.array([1.22771144e-10, 5.09703519e-10, 7.77414421e-10, 7.77414421e-10,
9.54941949e-10, 9.54941949e-10, 1.31838597e-09, 1.68301591e-09,
1.68301591e-09, 1.90734690e-09, 1.90734690e-09, 2.49398698e-09,
2.63214649e-09, 5.55011910e-09, 8.25271609e-09, 1.79101190e-08,
9.23263305e+05, 1.05796436e+06, 1.16544156e+06, 1.39483644e+06,
1.83988792e+06, 2.10385396e+06, 2.37875879e+06, 2.47138866e+06,
2.64517713e+06, 2.68781067e+06, 2.82400957e+06, 2.86645483e+06,
2.94682035e+06, 2.96132874e+06, 3.08410148e+06, 3.29632639e+06,
3.66669038e+06, 3.76286040e+06, 3.81791306e+06, 3.86566785e+06,
3.88861934e+06, 4.72670353e+06, 4.96458456e+06, 6.53314879e+06,
1.01442686e+07, 1.11366689e+07, 1.26150705e+07, 1.30030291e+07,
1.36655922e+07, 1.49308924e+07, 1.58259068e+07, 1.81000865e+07,
2.09105990e+07, 2.17439110e+07, 2.46822193e+07, 2.64512967e+07,
2.72379830e+07, 3.34861817e+07, 3.44988014e+07, 3.68957880e+07,
5.08236925e+07, 9.43118724e+07, 1.36087264e+08, 6.30301385e+08] )
#OLD
#[6.11142452e-11, 5.84760068e-10, 6.73066231e-10, 6.89070172e-10,
# 6.89070172e-10, 1.16844369e-09, 1.99649746e-09, 1.99649746e-09,
# 2.41697718e-09, 2.41697718e-09, 2.80832759e-09, 4.29148412e-09,
# 4.29148412e-09, 6.59655396e-09, 6.59655396e-09, 6.76915973e-09,
# 9.23087831e+05, 1.05783101e+06, 1.16457705e+06, 1.39492929e+06,
# 1.84015484e+06, 2.10613947e+06, 2.37963392e+06, 2.47192689e+06,
# 2.64566761e+06, 2.68722871e+06, 2.82383377e+06, 2.86584033e+06,
# 2.94590436e+06, 2.96180212e+06, 3.08322015e+06, 3.29389050e+06,
# 3.66581786e+06, 3.76266448e+06, 3.81921738e+06, 3.86624688e+06,
# 3.89045873e+06, 4.72831630e+06, 4.96416855e+06, 6.53286834e+06,
# 1.01424911e+07, 1.11347312e+07, 1.26152967e+07, 1.30081040e+07,
# 1.36647082e+07, 1.49293583e+07, 1.58234599e+07, 1.80999182e+07,
# 2.09155048e+07, 2.17444267e+07, 2.46870311e+07, 2.64427393e+07,
# 2.72410297e+07, 3.34988002e+07, 3.45005948e+07, 3.69040745e+07,
# 5.08647137e+07, 9.43153151e+07, 1.36088308e+08, 6.30304807e+08] )
TOL = 1e-7
for val,chk in zip(eigvals,eigvals_chk):
if abs(val) > TOL or abs(chk) > TOL:
self.assertAlmostEqual(abs(val-chk)/(abs(chk)+TOL), 0.0, places=3)
# (else both chk and val are <= TOL, so both == 0 for our purposes)
#print "eigvals = ",eigvals
def test_confidenceRegion(self):
res = pygsti.obj.Results()
res.init_dataset(self.ds)
res.init_circuits(self.gss)
#Add estimate for hessian-based CI --------------------------------------------------
res.add_estimate(stdxyi.target_model(), stdxyi.target_model(),
[self.model]*len(self.maxLengthList), parameters={'objective': 'logl'},
estimate_key="default")
est = res.estimates['default']
est.add_confidence_region_factory('final iteration estimate', 'final')
self.assertWarns(est.add_confidence_region_factory, 'final iteration estimate','final') #overwrites former
self.assertTrue( est.has_confidence_region_factory('final iteration estimate', 'final'))
cfctry = est.get_confidence_region_factory('final iteration estimate', 'final')
self.assertFalse( cfctry.can_construct_views() ) # b/c no hessian or LR enabled yet...
cfctry.compute_hessian(approximate=True)
cfctry.compute_hessian()
self.assertTrue( cfctry.has_hessian() )
self.assertFalse( cfctry.can_construct_views() ) # b/c hessian isn't projected yet...
mdl_dummy = cfctry.get_model() # test method
s = pickle.dumps(cfctry) # test pickle
pickle.loads(s)
cfctry.project_hessian('std')
cfctry.project_hessian('none')
cfctry.project_hessian('optimal gate CIs')
cfctry.project_hessian('intrinsic error')
with self.assertRaises(ValueError):
cfctry.project_hessian(95.0, 'normal', 'FooBar') #bad hessianProjection
self.assertTrue( cfctry.can_construct_views() )
ci_std = cfctry.view( 95.0, 'normal', 'std')
ci_noproj = cfctry.view( 95.0, 'normal', 'none')
ci_intrinsic = cfctry.view( 95.0, 'normal', 'intrinsic error')
ci_opt = cfctry.view( 95.0, 'normal', 'optimal gate CIs')
with self.assertRaises(ValueError):
cfctry.view(95.0, 'foobar') #bad region type
self.assertWarns(cfctry.view, 0.95, 'normal', 'none') # percentage < 1.0
#Add estimate for linresponse-based CI --------------------------------------------------
res.add_estimate(stdxyi.target_model(), stdxyi.target_model(),
[self.model]*len(self.maxLengthList), parameters={'objective': 'logl'},
estimate_key="linresponse")
estLR = res.estimates['linresponse']
#estLR.add_confidence_region_factory('final iteration estimate', 'final') #Could do this, but use alt. method for more coverage
with self.assertRaises(KeyError):
estLR.get_confidence_region_factory('final iteration estimate', 'final') #won't create by default
cfctryLR = estLR.get_confidence_region_factory('final iteration estimate', 'final', createIfNeeded=True) #now it will
self.assertTrue( estLR.has_confidence_region_factory('final iteration estimate', 'final'))
#cfctryLR = estLR.get_confidence_region_factory('final iteration estimate', 'final') #done by 'get' call above
self.assertFalse( cfctryLR.can_construct_views() ) # b/c no hessian or LR enabled yet...
cfctryLR.enable_linear_response_errorbars() #parent results object is used to automatically populate params
#self.assertTrue( cfctryLR.can_construct_views() )
ci_linresponse = cfctryLR.view( 95.0, 'normal', None)
mdl_dummy = cfctryLR.get_model() # test method
s = pickle.dumps(cfctryLR) # test pickle
pickle.loads(s)
#Add estimate for with bad objective ---------------------------------------------------------
res.add_estimate(stdxyi.target_model(), stdxyi.target_model(),
[self.model]*len(self.maxLengthList), parameters={'objective': 'foobar'},
estimate_key="foo")
est = res.estimates['foo']
est.add_confidence_region_factory('final iteration estimate', 'final')
with self.assertRaises(ValueError): # bad objective
est.get_confidence_region_factory('final iteration estimate', 'final').compute_hessian()
# Now test each of the views we created above ------------------------------------------------
for ci_cur in (ci_std, ci_noproj, ci_opt, ci_intrinsic, ci_linresponse):
s = pickle.dumps(ci_cur) # test pickle
pickle.loads(s)
#linear response CI doesn't support profile likelihood intervals
if ci_cur is not ci_linresponse: # (profile likelihoods not implemented in this case)
ar_of_intervals_Gx = ci_cur.get_profile_likelihood_confidence_intervals(L("Gx"))
ar_of_intervals_rho0 = ci_cur.get_profile_likelihood_confidence_intervals(L("rho0"))
ar_of_intervals_M0 = ci_cur.get_profile_likelihood_confidence_intervals(L("Mdefault"))
ar_of_intervals = ci_cur.get_profile_likelihood_confidence_intervals()
with self.assertRaises(ValueError):
ci_cur.get_profile_likelihood_confidence_intervals("foobar") #invalid label
def fnOfGate_float(mx,b):
return float(mx[0,0])
def fnOfGate_complex(mx,b):
return complex(mx[0,0] + 1.0j)
def fnOfGate_0D(mx,b):
return np.array( float(mx[0,0]) )
def fnOfGate_1D(mx,b):
return mx[0,:]
def fnOfGate_2D(mx,b):
return mx[:,:]
def fnOfGate_2D_complex(mx,b):
return np.array(mx[:,:] + 1j*mx[:,:],'complex')
def fnOfGate_3D(mx,b):
return np.zeros( (2,2,2), 'd') #just to test for error
fns = (fnOfGate_float, fnOfGate_0D, fnOfGate_1D,
fnOfGate_2D, fnOfGate_3D)
if ci_cur is not ci_linresponse: # complex functions not supported by linresponse CIs
fns += (fnOfGate_complex, fnOfGate_2D_complex)
for fnOfOp in fns:
FnClass = gsf.opfn_factory(fnOfOp)
FnObj = FnClass(self.model, 'Gx')
if fnOfOp is fnOfGate_3D:
with self.assertRaises(ValueError):
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
else:
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
df, f0 = self.runSilent(ci_cur.get_fn_confidence_interval,
FnObj, returnFnVal=True, verbosity=4)
##SHORT-CIRCUIT linear reponse here to reduce run time
if ci_cur is ci_linresponse: continue
def fnOfVec_float(v,b):
return float(v[0])
def fnOfVec_0D(v,b):
return np.array( float(v[0]) )
def fnOfVec_1D(v,b):
return np.array(v[:])
def fnOfVec_2D(v,b):
return np.dot(v.T,v)
def fnOfVec_3D(v,b):
return np.zeros( (2,2,2), 'd') #just to test for error
for fnOfVec in (fnOfVec_float, fnOfVec_0D, fnOfVec_1D, fnOfVec_2D, fnOfVec_3D):
FnClass = gsf.vecfn_factory(fnOfVec)
FnObj = FnClass(self.model, 'rho0', 'prep')
if fnOfVec is fnOfVec_3D:
with self.assertRaises(ValueError):
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
else:
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
df, f0 = self.runSilent(ci_cur.get_fn_confidence_interval,
FnObj, returnFnVal=True, verbosity=4)
for fnOfVec in (fnOfVec_float, fnOfVec_0D, fnOfVec_1D, fnOfVec_2D, fnOfVec_3D):
FnClass = gsf.vecfn_factory(fnOfVec)
FnObj = FnClass(self.model, 'Mdefault:0', 'effect')
if fnOfVec is fnOfVec_3D:
with self.assertRaises(ValueError):
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
else:
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
df, f0 = self.runSilent(ci_cur.get_fn_confidence_interval,
FnObj, returnFnVal=True, verbosity=4)
def fnOfSpam_float(rhoVecs, povms):
lbls = list(povms[0].keys())
return float( np.dot( rhoVecs[0].T, povms[0][lbls[0]] ) )
def fnOfSpam_0D(rhoVecs, povms):
lbls = list(povms[0].keys())
return np.array( float( np.dot( rhoVecs[0].T, povms[0][lbls[0]] ) ) )
def fnOfSpam_1D(rhoVecs, povms):
lbls = list(povms[0].keys())
return np.array( [ np.dot( rhoVecs[0].T, povms[0][lbls[0]] ), 0] )
def fnOfSpam_2D(rhoVecs, povms):
lbls = list(povms[0].keys())
return np.array( [[ np.dot( rhoVecs[0].T, povms[0][lbls[0]] ), 0],[0,0]] )
def fnOfSpam_3D(rhoVecs, povms):
return np.zeros( (2,2,2), 'd') #just to test for error
for fnOfSpam in (fnOfSpam_float, fnOfSpam_0D, fnOfSpam_1D, fnOfSpam_2D, fnOfSpam_3D):
FnClass = gsf.spamfn_factory(fnOfSpam)
FnObj = FnClass(self.model)
if fnOfSpam is fnOfSpam_3D:
with self.assertRaises(ValueError):
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
else:
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
df, f0 = self.runSilent(ci_cur.get_fn_confidence_interval,
FnObj, returnFnVal=True, verbosity=4)
def fnOfGateSet_float(mdl):
return float( mdl.operations['Gx'][0,0] )
def fnOfGateSet_0D(mdl):
return np.array( mdl.operations['Gx'][0,0] )
def fnOfGateSet_1D(mdl):
return np.array( mdl.operations['Gx'][0,:] )
def fnOfGateSet_2D(mdl):
return np.array( mdl.operations['Gx'] )
def fnOfGateSet_3D(mdl):
return np.zeros( (2,2,2), 'd') #just to test for error
for fnOfGateSet in (fnOfGateSet_float, fnOfGateSet_0D, fnOfGateSet_1D, fnOfGateSet_2D, fnOfGateSet_3D):
FnClass = gsf.modelfn_factory(fnOfGateSet)
FnObj = FnClass(self.model)
if fnOfGateSet is fnOfGateSet_3D:
with self.assertRaises(ValueError):
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
else:
df = ci_cur.get_fn_confidence_interval(FnObj, verbosity=0)
df, f0 = self.runSilent(ci_cur.get_fn_confidence_interval,
FnObj, returnFnVal=True, verbosity=4)
#TODO: assert values of df & f0 ??
def tets_pickle_ConfidenceRegion(self):
res = pygsti.obj.Results()
res.init_dataset(self.ds)
res.init_circuits(self.gss)
res.add_estimate(stdxyi.target_model(), stdxyi.target_model(),
[self.model]*len(self.maxLengthList), parameters={'objective': 'logl'},
estimate_key="default")
res.add_confidence_region_factory('final iteration estimate', 'final')
self.assertTrue( res.has_confidence_region_factory('final iteration estimate', 'final'))
cfctry = res.get_confidence_region_factory('final iteration estimate', 'final')
cfctry.compute_hessian()
self.assertTrue( cfctry.has_hessian() )
cfctry.project_hessian('std')
ci_std = cfctry.view( 95.0, 'normal', 'std')
s = pickle.dumps(cfctry)
cifctry2 = pickle.loads(s)
s = pickle.dumps(ci_std)
ci_std2 = pickle.loads(s)
#TODO: make sure ci_std and ci_std2 are the same
def test_mapcalc_hessian(self):
chi2, chi2Hessian = pygsti.chi2(self.model, self.ds,
returnHessian=True)
mdl_mapcalc = self.model.copy()
mdl_mapcalc._calcClass = MapForwardSimulator
chi2, chi2Hessian_mapcalc = pygsti.chi2(self.model, self.ds,
returnHessian=True)
self.assertArraysAlmostEqual(chi2Hessian, chi2Hessian_mapcalc)
if __name__ == "__main__":
unittest.main(verbosity=2)