def Test_model_error(self):
"""
Testing :meth:`bet.calculateP.calculateError.model_error`
"""
num = self.disc.check_nums()
m_error = calculateError.model_error(self.disc)
er_est = m_error.calculate_for_contour_events()
s_set = self.disc._input_sample_set.copy()
regions_local = np.equal(self.disc._io_ptr_local, 0)
s_set.set_region_local(regions_local)
s_set.local_to_global()
er_est2 = m_error.calculate_for_sample_set_region(s_set, 1)
self.assertAlmostEqual(er_est[0], er_est2)
error_id_sum_local = np.sum(
self.disc._input_sample_set._error_id_local)
error_id_sum = comm.allreduce(error_id_sum_local, op=MPI.SUM)
self.assertAlmostEqual(er_est2, error_id_sum)
emulated_set = self.disc._input_sample_set
er_est3 = m_error.calculate_for_sample_set_region(
s_set, 1, emulated_set=emulated_set)
self.assertAlmostEqual(er_est[0], er_est3)
self.disc.set_emulated_input_sample_set(self.disc._input_sample_set)
m_error = calculateError.model_error(self.disc)
er_est4 = m_error.calculate_for_sample_set_region(s_set, 1)
self.assertAlmostEqual(er_est[0], er_est4)
def Test_model_error(self):
"""
Testing :meth:`bet.calculateP.calculateError.model_error`
"""
num = self.disc.check_nums()
m_error = calculateError.model_error(self.disc)
er_est = m_error.calculate_for_contour_events()
s_set = self.disc._input_sample_set.copy()
regions_local = np.equal(self.disc._io_ptr_local, 0)
s_set.set_region_local(regions_local)
s_set.local_to_global()
er_est2 = m_error.calculate_for_sample_set_region(s_set,
1)
self.assertAlmostEqual(er_est[0], er_est2)
error_id_sum_local = np.sum(
self.disc._input_sample_set._error_id_local)
error_id_sum = comm.allreduce(error_id_sum_local, op=MPI.SUM)
self.assertAlmostEqual(er_est2, error_id_sum)
emulated_set = self.disc._input_sample_set
er_est3 = m_error.calculate_for_sample_set_region(s_set,
1,
emulated_set=emulated_set)
self.assertAlmostEqual(er_est[0], er_est3)
self.disc.set_emulated_input_sample_set(self.disc._input_sample_set)
m_error = calculateError.model_error(self.disc)
er_est4 = m_error.calculate_for_sample_set_region(s_set,
1)
self.assertAlmostEqual(er_est[0], er_est4)
def calculate_prob_for_sample_set_region(self,
s_set,
regions,
update_input=True):
"""
Solves stochastic inverse problem based on surrogate points and the
MC assumption. Calculates the probability of a regions of input space
and error estimates for those probabilities.
:param: s_set: sample set for which to calculate error
:type s_set: :class:`bet.sample.sample_set_base`
:param region: list of regions of s_set for which to calculate error
:type region: list
:param update_input: whether or not to update probabilities and
errror identifiers for input discretization
:type update_input: bool
:rtype: tuple
:returns: (probabilities, ``error_estimates``), the probability and
error estimates for the region
"""
if not hasattr(self, 'surrogate_discretization'):
msg = "surrogate discretization has not been created"
raise calculateError.wrong_argument_type(msg)
if not isinstance(s_set, sample.sample_set_base):
msg = "s_set must be of type bet.sample.sample_set_base"
raise calculateError.wrong_argument_type(msg)
# Calculate probability of region
if self.surrogate_discretization._input_sample_set._volumes_local\
is None:
self.surrogate_discretization._input_sample_set.\
estimate_volume_mc(globalize=False)
calculateP.prob(self.surrogate_discretization, globalize=False)
prob_new_values = calculateP.prob_from_sample_set(\
self.surrogate_discretization._input_sample_set, s_set)
# Calculate for each region
probabilities = []
error_estimates = []
for region in regions:
marker = np.equal(s_set._region, region)
probability = np.sum(prob_new_values[marker])
# Calculate error estimate for region
model_error = calculateError.model_error(\
self.surrogate_discretization)
error_estimate = model_error.calculate_for_sample_set_region_mc(\
s_set, region)
probabilities.append(probability)
error_estimates.append(error_estimate)
# Update input only if 1 region is given
if update_input:
num = self.input_disc._input_sample_set.check_num()
prob = np.zeros((num, ))
error_id = np.zeros((num, ))
for i in range(num):
Itemp = np.equal(self.dummy_disc._emulated_ii_ptr_local, i)
prob_sum = np.sum(self.surrogate_discretization.\
_input_sample_set._probabilities_local[Itemp])
prob[i] = comm.allreduce(prob_sum, op=MPI.SUM)
error_id_sum = np.sum(self.surrogate_discretization.\
_input_sample_set._error_id_local[Itemp])
error_id[i] = comm.allreduce(error_id_sum, op=MPI.SUM)
self.input_disc._input_sample_set.set_probabilities(prob)
self.input_disc._input_sample_set.set_error_id(error_id)
return (probabilities, error_estimates)
def calculate_prob_for_sample_set_region(self, s_set,
regions, update_input=True):
"""
Solves stochastic inverse problem based on surrogate points and the
MC assumption. Calculates the probability of a regions of input space
and error estimates for those probabilities.
:param: s_set: sample set for which to calculate error
:type s_set: :class:`bet.sample.sample_set_base`
:param region: list of regions of s_set for which to calculate error
:type region: list
:param update_input: whether or not to update probabilities and
errror identifiers for input discretization
:type update_input: bool
:rtype: tuple
:returns: (probabilities, ``error_estimates``), the probability and
error estimates for the region
"""
if not hasattr(self, 'surrogate_discretization'):
msg = "surrogate discretization has not been created"
raise calculateError.wrong_argument_type(msg)
if not isinstance(s_set, sample.sample_set_base):
msg = "s_set must be of type bet.sample.sample_set_base"
raise calculateError.wrong_argument_type(msg)
# Calculate probability of region
if self.surrogate_discretization._input_sample_set._volumes_local\
is None:
self.surrogate_discretization._input_sample_set.\
estimate_volume_mc(globalize=False)
calculateP.prob(self.surrogate_discretization, globalize=False)
prob_new_values = calculateP.prob_from_sample_set(\
self.surrogate_discretization._input_sample_set, s_set)
# Calculate for each region
probabilities = []
error_estimates = []
for region in regions:
marker = np.equal(s_set._region, region)
probability = np.sum(prob_new_values[marker])
# Calculate error estimate for region
model_error = calculateError.model_error(\
self.surrogate_discretization)
error_estimate = model_error.calculate_for_sample_set_region_mc(\
s_set, region)
probabilities.append(probability)
error_estimates.append(error_estimate)
# Update input only if 1 region is given
if update_input:
num = self.input_disc._input_sample_set.check_num()
prob = np.zeros((num,))
error_id = np.zeros((num,))
for i in range(num):
Itemp = np.equal(self.dummy_disc._emulated_ii_ptr_local, i)
prob_sum = np.sum(self.surrogate_discretization.\
_input_sample_set._probabilities_local[Itemp])
prob[i] = comm.allreduce(prob_sum, op=MPI.SUM)
error_id_sum = np.sum(self.surrogate_discretization.\
_input_sample_set._error_id_local[Itemp])
error_id[i] = comm.allreduce(error_id_sum, op=MPI.SUM)
self.input_disc._input_sample_set.set_probabilities(prob)
self.input_disc._input_sample_set.set_error_id(error_id)
return (probabilities, error_estimates)
P_approx = calculateP.prob_from_sample_set(my_disc._input_sample_set,
s_set)[0]
if comm.rank == 0:
print "Approximate probability: ", P_approx
# Calculate sampling error
samp_er = calculateError.sampling_error(my_disc)
(up, low) = samp_er.calculate_for_sample_set_region(s_set,
region=0, emulated_set=emulated_inputs)
if comm.rank == 0:
print "Sampling error upper bound: ", up
print "Sampling error lower bound: ", low
# Calculate modeling error
mod_er = calculateError.model_error(my_disc)
er_est = mod_er.calculate_for_sample_set_region(s_set,
region=0, emulated_set=emulated_inputs)
if comm.rank == 0:
print "Modeling error estimate: ", er_est
# Make piecewise constant surrogate of discretization
sur = surrogates.piecewise_polynomial_surrogate(my_disc)
sur_disc_constant = sur.generate_for_input_set(emulated_inputs, order=0)
# Calculate probability and error estimate
(P2, er_est2) = sur.calculate_prob_for_sample_set_region(s_set,
regions=[0])
if comm.rank == 0:
print "Piecewise constant surrogate probability: ", P2[0]
print "Piecewise constant error estimate ", er_est2[0]
print "Piecewise constant corrected probability: ", P2[0]-er_est2[0]
P_approx = calculateP.prob_from_sample_set(my_disc._input_sample_set, s_set)[0]
if comm.rank == 0:
print "Approximate probability: ", P_approx
# Calculate sampling error
samp_er = calculateError.sampling_error(my_disc)
(up,
low) = samp_er.calculate_for_sample_set_region(s_set,
region=0,
emulated_set=emulated_inputs)
if comm.rank == 0:
print "Sampling error upper bound: ", up
print "Sampling error lower bound: ", low
# Calculate modeling error
mod_er = calculateError.model_error(my_disc)
er_est = mod_er.calculate_for_sample_set_region(s_set,
region=0,
emulated_set=emulated_inputs)
if comm.rank == 0:
print "Modeling error estimate: ", er_est
# Make piecewise constant surrogate of discretization
sur = surrogates.piecewise_polynomial_surrogate(my_disc)
sur_disc_constant = sur.generate_for_input_set(emulated_inputs, order=0)
# Calculate probability and error estimate
(P2, er_est2) = sur.calculate_prob_for_sample_set_region(s_set, regions=[0])
if comm.rank == 0:
print "Piecewise constant surrogate probability: ", P2[0]
print "Piecewise constant error estimate ", er_est2[0]
print "Piecewise constant corrected probability: ", P2[0] - er_est2[0]
