def validateAICModelSelection(DummyEmp):
"""Create a DataFrame containing AIC scores of all standard models. Only NoGender at this stage.
Parameters
----------
DumyEmp : pandas DataFrame, Dummy emperical data frame as generated by generateDummyDataFrame()
Returns:
pandas DataFrame, Sorted AIC scores for different Delta's and model types
"""
AICFrame = pd.DataFrame(columns = ['Delta','k','ll','AIC'])
CatEmp = DummyEmp.mul(1)
Deltas = [1,2,3,4,5,10]
#Generate all the refactorings:
CatEmps = [ oboM.changePeriod(CatEmp,Delta) for Delta in Deltas]*2 #*2 as one for Dep and one for Indep
CatModsDep = [ oboM.generateDependentModelLaplace(CatEmp, Delta=Delta) for Delta in Deltas ]
CatModsIndep = [ oboM.generateIndependentModel(CatEmp, Delta=Delta) for Delta in Deltas ]
CatMods = CatModsDep + CatModsIndep
#For all generated model types on all deltas
for c,CatMod in enumerate(CatMods):
ModType = 'Dependent' if (c < len(CatMods)/2) else 'Independent'
if 'Dependent' in ModType:
k = (9-1)*(7-1)*CatMod.shape[0]
elif 'Independent' in ModType:
k = (9+7-2)*CatMod.shape[0]
else:
print('Oh deary me')
return
ll = oboM.loglikilyhood(CatEmps[c], CatMod)
AIC = 2*k - 2*ll
AICFrame.loc[c] = [ ModType + ' Delta = ' + str(Deltas[int(c%(len(CatMods)/2))]), k, ll, AIC]
##print('{}, years: {}, model index {}, k {}, log-likelihood {}, AIC {}'.format(file,fileYearDelta,DeltasIndex,k,ll, AIC))
##print('{} loaded'.format(file))
#except:
#print('{} failed to load'.format(file))
##Return the frame
return AICFrame.sort('AIC')
