def test_memory(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
gateset = pygsti.io.load_gateset(compare_files + "/analysis.gateset")
with self.assertRaises(MemoryError):
pygsti.logl_hessian(gateset, ds,
probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False, memLimit=0) # No memory for you
L = pygsti.logl_hessian(gateset, ds, probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False, memLimit=None, verbosity=10) # Reference: no mem limit
L1 = pygsti.logl_hessian(gateset, ds, probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False, memLimit=370000000, verbosity=10) # Limit memory a bit
L2 = pygsti.logl_hessian(gateset, ds,probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False, memLimit=1000000, verbosity=10) # Limit memory a bit more
L3 = pygsti.logl_hessian(gateset, ds, probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False, memLimit=300000, verbosity=10) # Very low memory (splits tree)
with self.assertRaises(MemoryError):
pygsti.logl_hessian(gateset, ds,
probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False, memLimit=100000) # Splitting unproductive
#print("****DEBUG LOGL HESSIAN L****")
#print("shape = ",L.shape)
#to_check = L
#for i in range(L.shape[0]):
# for j in range(L.shape[1]):
# diff = abs(L3[i,j]-L[i,j])
# if diff > 1e-6:
# print("[%d,%d] diff = %g - %g = %g" % (i,j,L3[i,j],L[i,j],L3[i,j]-L[i,j]))
self.assertArraysAlmostEqual(L, L1)
self.assertArraysAlmostEqual(L, L2)
self.assertArraysAlmostEqual(L, L3)
def test_memory(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files +
"/analysis.dataset")
with self.assertRaises(MemoryError):
pygsti.logl_hessian(std.gs_target,
ds,
probClipInterval=(-1e6, 1e6),
countVecMx=None,
poissonPicture=True,
check=False,
memLimit=0) # No memory for you
L = pygsti.logl_hessian(std.gs_target,
ds,
probClipInterval=(-1e6, 1e6),
countVecMx=None,
poissonPicture=True,
check=False,
memLimit=370000000) # Limit memory a bit
pygsti.logl_hessian(std.gs_target,
ds,
probClipInterval=(-1e6, 1e6),
countVecMx=None,
poissonPicture=True,
check=False,
memLimit=25000000) # Limit memory a bit more
with self.assertRaises(MemoryError):
pygsti.logl_hessian(
std.gs_target,
ds,
probClipInterval=(-1e6, 1e6),
countVecMx=None,
poissonPicture=True,
check=False,
memLimit=30000) # Until another error is thrown
def test_hessian_mpi(self):
from mpi4py import MPI
comm = MPI.COMM_WORLD
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
L = pygsti.logl_hessian(std.gs_target, ds,
probClipInterval=(-1e6,1e6), countVecMx=None, memLimit=25000000,
poissonPicture=True, check=False, comm=comm)
print(L)
def test_logl_fn(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
gatestrings = pygsti.construction.gatestring_list( [ ('Gx',), ('Gy',), ('Gx','Gx') ] )
spam_labels = std.gs_target.get_spam_labels()
pygsti.create_count_vec_dict( spam_labels, ds, gatestrings )
gateset = pygsti.io.load_gateset(compare_files + "/analysis.gateset")
L1 = pygsti.logl(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False)
L2 = pygsti.logl(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=False, check=False) #Non-poisson-picture
dL1 = pygsti.logl_jacobian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
dL2 = pygsti.logl_jacobian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
dL2b = pygsti.logl_jacobian(gateset, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
hL1 = pygsti.logl_hessian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
hL2 = pygsti.logl_hessian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
hL2b = pygsti.logl_hessian(gateset, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
maxL1 = pygsti.logl_max(ds, gatestrings, poissonPicture=True, check=True)
maxL2 = pygsti.logl_max(ds, gatestrings, poissonPicture=False, check=True)
pygsti.cptp_penalty(gateset, include_spam_penalty=True)
twoDelta1 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=True)
twoDelta2 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=False)
def test_hessian_mpi(self):
from mpi4py import MPI
comm = MPI.COMM_WORLD
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
gateset = pygsti.io.load_gateset(compare_files + "/analysis.gateset")
L = pygsti.logl_hessian(gateset, ds,
probClipInterval=(-1e6,1e6), memLimit=25000000,
poissonPicture=True, check=False, comm=comm)
print(L)
def test_logl_fn(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset%s" % self.versionsuffix)
circuits = pygsti.construction.circuit_list( [ ('Gx',), ('Gy',), ('Gx','Gx') ] )
#OLD spam_labels = std.target_model().get_spam_labels()
#OLD pygsti.create_count_vec_dict( spam_labels, ds, circuits )
model = pygsti.io.load_model(compare_files + "/analysis.model")
L1 = pygsti.logl(model, ds, circuits,
probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False)
L2 = pygsti.logl(model, ds, circuits,
probClipInterval=(-1e6,1e6),
poissonPicture=False, check=False) #Non-poisson-picture
dL1 = pygsti.logl_jacobian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
dL2 = pygsti.logl_jacobian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
dL2b = pygsti.logl_jacobian(model, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as mdl list
hL1 = pygsti.logl_hessian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
hL2 = pygsti.logl_hessian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
hL2b = pygsti.logl_hessian(model, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as mdl list
maxL1 = pygsti.logl_max(model, ds, circuits, poissonPicture=True, check=True)
maxL2 = pygsti.logl_max(model, ds, circuits, poissonPicture=False, check=True)
pygsti.cptp_penalty(model, include_spam_penalty=True)
twoDelta1 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=True)
twoDelta2 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=False)
def test_hessian_mpi(self):
from mpi4py import MPI
comm = MPI.COMM_WORLD
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset%s" % self.versionsuffix)
model = pygsti.io.load_model(compare_files + "/analysis.model")
L = pygsti.logl_hessian(model, ds,
probClipInterval=(-1e6,1e6), memLimit=25000000,
poissonPicture=True, check=False, comm=comm)
print(L)
def test_logl_fn(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
gatestrings = pygsti.construction.gatestring_list( [ ('Gx',), ('Gy',), ('Gx','Gx') ] )
spam_labels = std.gs_target.get_spam_labels()
pygsti.create_count_vec_dict( spam_labels, ds, gatestrings )
L1 = pygsti.logl(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False)
L2 = pygsti.logl(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=False, check=False) #Non-poisson-picture
dL1 = pygsti.logl_jacobian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
dL2 = pygsti.logl_jacobian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
dL2b = pygsti.logl_jacobian(std.gs_target, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
hL1 = pygsti.logl_hessian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
hL2 = pygsti.logl_hessian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
hL2b = pygsti.logl_hessian(std.gs_target, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
maxL1 = pygsti.logl_max(ds, gatestrings, poissonPicture=True, check=True)
maxL2 = pygsti.logl_max(ds, gatestrings, poissonPicture=False, check=True)
pygsti.cptp_penalty(std.gs_target, include_spam_penalty=True)
twoDelta1 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=True)
twoDelta2 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=False)
def test_memory(self):
def musage(prefix):
p = psutil.Process(os.getpid())
print(prefix, p.memory_info()[0])
current_mem = pygsti.baseobjs.profiler._get_mem_usage
musage("Initial")
ds = pygsti.data.DataSet(file_to_load_from=compare_files +
"/analysis.dataset")
model = pygsti.io.load_model(compare_files + "/analysis.model")
musage("Pt1")
with self.assertRaises(MemoryError):
pygsti.logl_hessian(model,
ds,
prob_clip_interval=(-1e6, 1e6),
poisson_picture=True,
mem_limit=0) # No memory for you
musage("Pt2")
L = pygsti.logl_hessian(model,
ds,
prob_clip_interval=(-1e6, 1e6),
poisson_picture=True,
mem_limit=None,
verbosity=10) # Reference: no mem limit
musage("Pt3")
L1 = pygsti.logl_hessian(model,
ds,
prob_clip_interval=(-1e6, 1e6),
poisson_picture=True,
mem_limit=1024.0**3,
verbosity=10) # Limit memory (1GB)
musage("Pt4")
#L2 = pygsti.logl_hessian(model, ds,prob_clip_interval=(-1e6,1e6),
# poisson_picture=True, mem_limit=current_mem()+1000000, verbosity=10) # Limit memory a bit more
#musage("Pt5")
#L3 = pygsti.logl_hessian(model, ds, prob_clip_interval=(-1e6,1e6),
# poisson_picture=True, mem_limit=current_mem()+300000, verbosity=10) # Very low memory (splits tree)
#We've currently disabled memory errors - re-enable this after we get memory checks working again.
#with self.assertRaises(MemoryError):
pygsti.logl_hessian(model,
ds,
prob_clip_interval=(-1e6, 1e6),
poisson_picture=True,
mem_limit=1024.0**3)
#print("****DEBUG LOGL HESSIAN L****")
#print("shape = ",L.shape)
#to_check = L
#for i in range(L.shape[0]):
# for j in range(L.shape[1]):
# diff = abs(L3[i,j]-L[i,j])
# if diff > 1e-6:
# print("[%d,%d] diff = %g - %g = %g" % (i,j,L3[i,j],L[i,j],L3[i,j]-L[i,j]))
self.assertArraysAlmostEqual(L, L1)
def test_hessian_mpi(self):
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
current_mem = pygsti.baseobjs.profiler._get_mem_usage
ds = pygsti.data.DataSet(file_to_load_from=compare_files +
"/analysis.dataset")
model = pygsti.io.load_model(compare_files + "/analysis.model")
L = pygsti.logl_hessian(model,
ds,
prob_clip_interval=(-1e6, 1e6),
mem_limit=500 * 1024**2 + current_mem(),
poisson_picture=True,
comm=comm)
print(L)
except ImportError:
self.skipTest('Skipping because failed to import MPI')
def test_memory(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
with self.assertRaises(MemoryError):
pygsti.logl_hessian(std.gs_target, ds,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False, memLimit=0) # No memory for you
L = pygsti.logl_hessian(std.gs_target, ds,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False, memLimit=370000000) # Limit memory a bit
pygsti.logl_hessian(std.gs_target, ds,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False, memLimit=25000000) # Limit memory a bit more
with self.assertRaises(MemoryError):
pygsti.logl_hessian(std.gs_target, ds,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False, memLimit=30000) # Until another error is thrown
