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Updated iterative_fitter.py to calculate error for all species. Added new test with mean squared error.

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titusquah
2020-07-17 12:01:04 -06:00
parent 4fa69a9b44
commit 6a4aad4266
11 changed files with 743 additions and 81 deletions
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26
llepe/objectives.py
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@@ -57,3 +57,29 @@ def ind_lmse_perturbed_obj(predicted_dict,
objectives.append(np.mean(fun1))
objectives = np.array(objectives)
return objectives
def mean_squared_error(predicted_dict,
measured_df,
species_list):
meas_aq = np.concatenate([measured_df['{0}_aq_eq'.format(species)].values
for species in species_list])
pred_aq = np.concatenate([
predicted_dict['{0}_aq_eq'.format(species)]
for species in species_list])
meas_d = np.concatenate([measured_df['{0}_d_eq'.format(species)].values
for species in species_list])
pred_d = np.concatenate([
predicted_dict['{0}_d_eq'.format(species)]
for species in species_list])
meas_org = meas_aq * meas_d
pred_org = np.concatenate([
predicted_dict['{0}_org_eq'.format(species)]
for species in species_list])
aq_obj = (meas_aq - pred_aq)**2
org_obj = (meas_org - pred_org)**2
objs = np.concatenate([aq_obj, org_obj])
obj = np.mean(objs)
return obj