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Che ki peuq integration v3 #10

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Jan 28, 2021
Merged

Che ki peuq integration v3 #10

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16240dd
Cleaning up the merge conflict that was dropped
AdityaSavara Jan 28, 2021
abead3e
Remoing print statement from CheKiPUEQ_local
AdityaSavara Jan 28, 2021
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3 changes: 0 additions & 3 deletions src/optimize/CheKiPEUQ_from_Frhodo.py
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Expand Up @@ -161,8 +161,5 @@ def get_consolidated_parameters_arrays(rate_constants_values, rate_constants_low
return pars_values, pars_lower_bnds, pars_upper_bnds, pars_bnds_exist, unbounded_indices

def remove_unbounded_values(array_to_truncate, unbounded_indices):
print("line 164", unbounded_indices, array_to_truncate)
truncated_array = np.delete(array_to_truncate, unbounded_indices, axis=0)
print("line 164", unbounded_indices, truncated_array)

return truncated_array
1 change: 0 additions & 1 deletion src/optimize/CheKiPEUQ_local/InverseProblem.py
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Expand Up @@ -110,7 +110,6 @@ def __init__(self, UserInput = None):
#We will also fill the model['InputParameterPriorValues'] to have the mean of the two bounds. This can matter for some of the scaling that occurs later.
self.UserInput.mu_prior[parameterIndex] = (UserInput.model['InputParameterPriorValues_upperBounds'][parameterIndex] + UserInput.model['InputParameterPriorValues_lowerBounds'][parameterIndex])/2

print("line 112 of CheKiPEUQ", UserInput.InputParametersPriorValuesUncertainties)
#Now to make covmat. Leaving the original dictionary object intact, but making a new object to make covmat_prior.
if len(np.shape(UserInput.InputParametersPriorValuesUncertainties)) == 1 and (len(UserInput.InputParametersPriorValuesUncertainties) > 0): #If it's a 1D array/list that is filled, we'll diagonalize it.
UserInput.std_prior = np.array(UserInput.InputParametersPriorValuesUncertainties, dtype='float') #using 32 since not everyone has 64.
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3 changes: 1 addition & 2 deletions src/optimize/fit_fcn.py
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Expand Up @@ -450,7 +450,7 @@ def get_last_obs_sim_interp(varying_rate_vals): #A. Savara added this. It needs
SSE = generalized_loss_fcn(loss_resid, mu=loss_min)
SSE = rescale_loss_fcn(loss_resid, SSE)
exp_loss_weights = loss_exp/SSE # comparison is between selected loss fcn and SSE (L2 loss)
Bayesian_dict['weights_data'] = np.concatenate(aggregate_weights*exp_loss_weights, axis=0)
Bayesian_dict['weights_data'] = np.concatenate(aggregate_weights*exp_loss_weights, axis=0).flatten()

#Bayesian_dict['weights_data'] /= np.max(Bayesian_dict['weights_data']) # if we want to normalize by maximum

Expand Down Expand Up @@ -491,7 +491,6 @@ def get_last_obs_sim_interp(varying_rate_vals): #A. Savara added this. It needs
log_posterior_density = optimize.CheKiPEUQ_from_Frhodo.get_log_posterior_density(CheKiPEUQ_PE_object, Bayesian_dict['pars_current_guess_truncated'])
#Step 5 of Bayesian: return the objective function and any other metrics desired.
obj_fcn = -1*log_posterior_density #need neg_logP because minimizing.
print("line 481 of fit_fcn, Bayesian obj_fcn", obj_fcn)

# For updating
self.i += 1
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