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Merge pull request #308 from AdityaSavara/develop
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Develop
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AdityaSavara authored Aug 8, 2022
2 parents f8443ca + 9a04db0 commit 8af4087
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8 changes: 7 additions & 1 deletion PEUQSE/InverseProblem.py
Original file line number Diff line number Diff line change
Expand Up @@ -2752,10 +2752,12 @@ def getLogLikelihood(self,discreteParameterVector): #The variable discreteParame
simulatedResponses = self.getSimulatedResponses(discreteParameterVectorTuple)
#Failure checks:
if type(simulatedResponses) == type(None):
print("Warning: There are likelihood points that have zero probability due to receiving a None type back for the simulated responses. If there are too many points like this during an MCMC or permutationsToSamples run, the MAP and mu_AP returned will not be meaningful. Parameters: " + str(discreteParameterVectorTuple))
return float('-inf'), None #This is intended for the case that the simulation fails, indicated by receiving an 'nan' or None type from user's simulation function.
#Check if there are any 'nan' in the simulations array, and treat that as a failure also.
nans_in_array = np.isnan(simulatedResponses)
if True in nans_in_array:
print("Warning: There are likelihood points that have zero probability due to not a number ('nan') values in the simulated responses. If there are too many points like this, the MAP and mu_AP returned will not be meaningful. Parameters: " + str(discreteParameterVectorTuple))
return float('-inf'), None
#need to check if there are any 'responses_simulation_uncertainties'.
if type(self.UserInput.responses_simulation_uncertainties) == type(None): #if it's a None type, we keep it as a None type
Expand Down Expand Up @@ -2809,10 +2811,14 @@ def getLogLikelihood_byResponses(self, simulatedResponses_transformed, simulated
comprehensive_responses_covmat_shape = copy.deepcopy(observed_responses_covmat_transformed_shape) #no need to take the shape of the actual comprehensive_responses_covmat since they must be same. This is probably slightly less computation.
if (len(comprehensive_responses_covmat_shape) == 1) and (comprehensive_responses_covmat_shape[0]==1): #Matrix is square because has only one value.
log_probability_metric = multivariate_normal.logpdf(mean=simulatedResponses_transformed,x=observedResponses_transformed,cov=comprehensive_responses_covmat)
if float(log_probability_metric) == float('-inf'):
print("Warning: There are likelihood points that have zero probability. If there are too many points like this, the MAP and mu_AP returned will not be meaningful. Parameters: " + str(discreteParameterVectorTuple))
return log_probability_metric, simulatedResponses_transformed #Return this rather than going through loop further.
elif len(comprehensive_responses_covmat_shape) > 1 and (comprehensive_responses_covmat_shape[0] == comprehensive_responses_covmat_shape[1]): #Else it is 2D, check if it's square.
try:
log_probability_metric = multivariate_normal.logpdf(mean=simulatedResponses_transformed,x=observedResponses_transformed,cov=comprehensive_responses_covmat)
if float(log_probability_metric) == float('-inf'):
print("Warning: There are likelihood points that have zero probability. If there are too many points like this, the MAP and mu_AP returned will not be meaningful. Parameters: " + str(discreteParameterVectorTuple))
return log_probability_metric, simulatedResponses_transformed #Return this rather than going through loop further.
except:
pass #If it failed, we assume it is not square. For example, it could be 2 responses of length 2 each, which is not actually square.
Expand Down Expand Up @@ -2846,7 +2852,7 @@ def getLogLikelihood_byResponses(self, simulatedResponses_transformed, simulated
current_log_probability_metric = float('-inf')
#response_log_probability_metric = current_log_probability_metric + response_log_probability_metric
if float(current_log_probability_metric) == float('-inf'):
print("Warning: There are posterior points that have zero probability. If there are too many points like this, the MAP and mu_AP returned will not be meaningful. Parameters:", discreteParameterVectorTuple)
warnings.warn("Warning: There are cases of sampling where a response value has zero probability in the likelihood. If there are too many points like this, the MAP and mu_AP returned will not be meaningful. ResponseIndex: " + str( responseIndex))
current_log_probability_metric = -1E100 #Just choosing an arbitrarily very severe penalty. I know that I have seen 1E-48 to -303 from the multivariate pdf, and values inbetween like -171, -217, -272. I found that -1000 seems to be worse, but I don't have a systematic testing. I think -1000 was causing numerical errors.
response_log_probability_metric = current_log_probability_metric + response_log_probability_metric
log_probability_metric = log_probability_metric + response_log_probability_metric
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2 changes: 1 addition & 1 deletion setup.py
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
AUTHOR = 'Aditya Savara'
REQUIRES_PYTHON = '>=3.5.0'

VERSION = '1.2.0'
VERSION = '1.2.1'

LICENSE = 'BSD-3-Clause'

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