diff --git a/p_1/code/66.py b/p_1/code/66.py
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+###******************************HALLO*******************************
+import pandas as pd
+data_encoded =pd.read_excel("data_encoded.xlsx")
+ml= ['EntertainmentVenuesPartial','RestaurantsCafesPartial','EntertainmentVenues','MassGatherAll','ClosSec','GymsSportsCentresPartial','ClosPrim',
+ 'NonEssentialShopsPartial','ClosPubAnyPartial','RestaurantsCafes','GymsSportsCentres','MassGather50','PrivateGatheringRestrictions',
+ 'MassGatherAllPartial',
+ 'ClosHigh','NonEssentialShops','ClosSecPartial','OutdoorOver500','ClosDaycare','BanOnAllEvents','IndoorOver500','QuarantineForInternationalTravellers',
+ 'ClosHighPartial','IndoorOver100','Teleworking','ClosPubAny','PlaceOfWorshipPartial','MasksMandatoryClosedSpacesPartial','MassGather50Partial',
+ 'StayHomeOrderPartial','OutdoorOver100','IndoorOver50','ClosPrimPartial','PrivateGatheringRestrictionsPartial','MasksMandatoryClosedSpaces',
+ 'OutdoorOver1000','TeleworkingPartial','MasksMandatoryAllSpaces','OutdoorOver50','StayHomeOrder','QuarantineForInternationalTravellersPartial',
+ 'MasksMandatoryAllSpacesPartial','StayHomeGen','PlaceOfWorship','ClosDaycarePartial','IndoorOver1000','BanOnAllEventsPartial',
+ 'HotelsOtherAccommodationPartial',
+ 'StayHomeRiskG','ClosureOfPublicTransportPartial','AdaptationOfWorkplace','HotelsOtherAccommodation','MasksVoluntaryClosedSpacesPartial',
+ 'RegionalStayHomeOrderPartial','AdaptationOfWorkplacePartial','MasksVoluntaryAllSpaces','MasksVoluntaryAllSpacesPartial','MasksVoluntaryClosedSpaces',
+ 'SocialCircle','WorkplaceClosures','RegionalStayHomeOrder','ClosureOfPublicTransport','StayHomeGenPartial','WorkplaceClosuresPartial',
+ 'StayHomeRiskGPartial','SocialCirclePartial']
+cs=['Netherlands','Czechia','Lithuania','Austria','Poland','Slovenia','Estonia','Italy','Slovakia','Ireland','Denmark',
+ 'Iceland','Cyprus','Greece','Belgium','Bulgaria','France','Germany','Latvia','Spain','Norway','Romania','Liechtenstein',
+ 'Portugal','Luxembourg','Hungary','Malta','Croatia','Finland','Sweden']
+###***************************BAYESIAN GOVERMENT COVID APPLICATION**************************************************WRITEN BY:
+###********************************************************************************************************HAMED KHALILI***********
+###***************************INPUTS OF THE PROGRAM********************************************************************************************
+ml.sort()
+import pandas as pd
+import base64
+
+
+azResults=[]
+Results=[]
+for cns in cs:
+ country=[cns]#
+ for tage in [7]:
+ for massnahme in ml:
+ incidence_days_number=tage
+ print(incidence_days_number)
+ X=[massnahme]
+ print(X)
+
+
+ #X=['MasksMandatoryAllSpaces','MasksMandatoryClosedSpaces']
+
+
+ method="hierarchical"#or#pooled#or#unpooled
+ ###********************************************************************************************************************************************
+ #['ClosDaycare','ClosDaycarePartial','ClosPrimPartial','ClosSecPartial','ClosPrim','ClosSec','ClosHighPartial','ClosHigh']
+ #['RestaurantsCafes','RestaurantsCafesPartial']
+ #['GymsSportsCentres','GymsSportsCentresPartial']
+ #['Teleworking','TeleworkingPartial','WorkplaceClosuresPartial','AdaptationOfWorkplace','AdaptationOfWorkplacePartial','WorkplaceClosures']
+ #['MasksMandatoryClosedSpacesPartial','MasksMandatoryAllSpaces','MasksMandatoryClosedSpaces','MasksMandatoryAllSpacesPartial']
+ #y_pred =this is almost identical to y_est except we do not specify the observed data. PyMC considers this to be a stochastic node
+ #(as opposed to an observed node) and as the MCMC sampler runs - it also samples data from y_est.
+
+ """
+ ['Netherlands','Czechia','Lithuania','Austria','Poland','Slovenia','Estonia','Italy','Slovakia','Ireland','Denmark',
+ 'Iceland','Cyprus','Greece','Belgium','Bulgaria','France','Germany','Latvia','Spain','Norway','Romania','Liechtenstein',
+ 'Portugal','Luxembourg','Hungary','Malta','Croatia','Finland','Sweden']
+
+ ['EntertainmentVenuesPartial','RestaurantsCafesPartial','EntertainmentVenues','MassGatherAll','ClosSec','GymsSportsCentresPartial','ClosPrim',
+ 'NonEssentialShopsPartial','ClosPubAnyPartial','RestaurantsCafes','GymsSportsCentres','MassGather50','PrivateGatheringRestrictions',
+ 'MassGatherAllPartial',
+ 'ClosHigh','NonEssentialShops','ClosSecPartial','OutdoorOver500','ClosDaycare','BanOnAllEvents','IndoorOver500','QuarantineForInternationalTravellers',
+ 'ClosHighPartial','IndoorOver100','Teleworking','ClosPubAny','PlaceOfWorshipPartial','MasksMandatoryClosedSpacesPartial','MassGather50Partial',
+ 'StayHomeOrderPartial','OutdoorOver100','IndoorOver50','ClosPrimPartial','PrivateGatheringRestrictionsPartial','MasksMandatoryClosedSpaces',
+ 'OutdoorOver1000','TeleworkingPartial','MasksMandatoryAllSpaces','OutdoorOver50','StayHomeOrder','QuarantineForInternationalTravellersPartial',
+ 'MasksMandatoryAllSpacesPartial','StayHomeGen','PlaceOfWorship','ClosDaycarePartial','IndoorOver1000','BanOnAllEventsPartial',
+ 'HotelsOtherAccommodationPartial',
+ 'StayHomeRiskG','ClosureOfPublicTransportPartial','AdaptationOfWorkplace','HotelsOtherAccommodation','MasksVoluntaryClosedSpacesPartial',
+ 'RegionalStayHomeOrderPartial','AdaptationOfWorkplacePartial','MasksVoluntaryAllSpaces','MasksVoluntaryAllSpacesPartial','MasksVoluntaryClosedSpaces',
+ 'SocialCircle','WorkplaceClosures','RegionalStayHomeOrder','ClosureOfPublicTransport','StayHomeGenPartial','WorkplaceClosuresPartial',
+ 'StayHomeRiskGPartial','SocialCirclePartial']
+
+
+ """
+ ###************MAIN BODY OF THE PROGRAM****************************************************************************************************
+
+ def add_elemant(element,lis,j):
+ for i in lis:
+ if element in i:
+ return i[j]
+
+
+ colors = ['#348ABD', '#A60628', '#7A68A6', '#467821', '#D55E00', '#CC79A7', '#56B4E9', '#009E73', '#F0E442', '#0072B2']
+ import pandas as pd
+ import datetime
+ from datetime import timedelta
+ import warnings
+ warnings.filterwarnings("ignore", category=FutureWarning)
+ import seaborn as sns
+ import arviz as az
+ import itertools
+ import matplotlib.pyplot as plt
+ import numpy as np
+ import pymc3 as pm
+ import scipy
+ import scipy.stats as stats
+ from IPython.display import Image
+ from sklearn import preprocessing
+ import pandas as pd
+ import datetime
+ from IPython.display import display
+
+ def f(r,tr):
+ for i in tr:
+
+ if i in eval(r):
+ return "+"#+str(tr).replace(",", " or")
+
+ else:
+ return "-"#+str(tr).replace(",", " or")
+ #if method=="hierarchical":
+ #liss=[]
+ responses_plus=[]
+ responses_minus=[]
+ #liss_plus = pd.DataFrame({'country':[],'mean':[],'std':[],'count+':[],'count-':[],'mean+':[],'mean-':[],'std+':[],'std-':[]})
+ #for idx, name in (enumerate(cb['countriesAndTerritories'].value_counts().index.tolist())):
+ for name in country:
+ land=name
+
+ CBook=data_encoded.loc[(data_encoded['countriesAndTerritories'] == land)]
+ CBook=CBook.reset_index()
+ CBook['rpn_minus_one'+str(incidence_days_number)] = pd.Series(dtype='float')
+ CBook = CBook.replace(np.nan, 0)
+ for i in range (0,len(CBook)):
+ #caesdayNplusone=CBook.loc[i+incidence_days_number, ['cases']].to_list()[0]
+ #averagecasesdayoneafter=sum(CBook.iloc[i:i+incidence_days_number]['cases'].values)/incidence_days_number
+ #averagecasesdayonebefore=sum(CBook.iloc[i-incidence_days_number:i]['cases'].values)/incidence_days_number
+ #if averagecasesdayonetoNminusone ==0:
+ #print("00000000")
+ CBook.loc[i, ['rpn_minus_one'+str(incidence_days_number)]]=CBook.iloc[i]['7days_after_mean']/CBook.iloc[i]['7days_before_mean']-1
+
+ mass='rpn_minus_one'+str(incidence_days_number)
+ CBook[str(X[0]).replace(",", " or")+''+ 'Rescd'] = CBook.apply(lambda row: land+f(row['Rescd'],X), axis=1)
+ #CBook[str(X[0]).replace(",", " or")+''+ 'Rescd'].value_counts()
+ m=CBook.iloc[:]['rpn_minus_one7'].mean()
+ s=CBook.iloc[:]['rpn_minus_one7'].std()
+
+ CBook_plus=CBook[(CBook.iloc[:][str(X[0]).replace(",", " or")+''+ 'Rescd'] == land+"+")]
+ CBook_minus=CBook[(CBook.iloc[:][str(X[0]).replace(",", " or")+''+ 'Rescd'] == land+"-")]
+ #print(len(CBook_minus))
+ df=CBook_minus
+ if 'Partial' not in massnahme:
+ mp=massnahme + 'Partial'
+ CBook_minus=df[df['Rescd'].apply(lambda x: mp not in eval(x) )]
+ #df_2
+ #df=df_2
+ #CBook_plus=df[df['Rescd'].apply(lambda x: massnahme in x)]
+ #CBook_minus=df[df['Rescd'].apply(lambda x: massnahme not in x )]
+ if 'Partial' in massnahme:
+ mp=massnahme[0:-7]
+ CBook_minus=df[df['Rescd'].apply(lambda x: mp not in eval(x) )]
+ le_plus = preprocessing.LabelEncoder()
+ le_minus = preprocessing.LabelEncoder()
+ rc=str(X[0]).replace(",", " or")+''+ 'Rescd'
+ clm_plus=CBook_plus[rc]
+ clm_minus=CBook_minus[rc]
+ response_idx_plus = le_plus.fit_transform(clm_plus)
+ response_idx_minus = le_minus.fit_transform(clm_minus)
+ response_plus = le_plus.classes_
+ response_minus = le_minus.classes_
+ #number_of_response_plus=len(response_plus)
+ #number_of_response_minus=len(response_minus)
+ #for i in range(0, number_of_response_codes):
+ if len(response_plus)==0:
+ response_plus=[land+"+",[]]
+ responses_plus.append(response_plus)
+ else:
+ response_plus[0]=[response_plus[0],CBook_plus[clm_plus==response_plus[0]][mass].values.tolist()]
+ responses_plus.append(response_plus[0])
+ if len(response_minus)==0:
+ response_minus=[land+"-",[]]
+ responses_minus.append(response_minus)
+ else:
+ response_minus[0]=[response_minus[0],CBook_minus[clm_minus==response_minus[0]][mass].values.tolist()]
+ responses_minus.append(response_minus[0])
+ responses=responses_minus+responses_plus
+ #s_plus=0
+ #s_minus=0
+ m_plus=CBook_plus.iloc[:]['rpn_minus_one7'].mean()
+ #if responses[1][1]!=[]:
+ s_plus=CBook_plus.iloc[:]['rpn_minus_one7'].std()
+ m_minus=CBook_minus.iloc[:]['rpn_minus_one7'].mean()
+ #if responses[0][1]!=[]:
+ s_minus=CBook_minus.iloc[:]['rpn_minus_one7'].std()
+ #responses=responses_minus+responses_plus
+ with pm.Model() as model:
+ hyper_mu_parameter=pm.Normal('hyper_mu_parameter', mu=m,sd=s)#
+ if responses[1][1]==[] or responses[0][1]==[]:
+ hyper_sd_parameter=s
+ #pm.Exponential("hyper_sd_parameter", lam=1/std_mean_positive)#
+ #hyper_sd_error_parameter=pm.Uniform('hyper_sd_error_parameter', lower=std_min_positive,upper=std_max_positive)
+ else:
+ hyper_sd_parameter=pm.Uniform('hyper_sd_parameter', lower=min(s_minus,s_plus),upper=max(s_minus,s_plus))
+
+ hyper_nu_parameter=pm.Uniform('hyper_nu_parameter', lower=0,upper=30)
+
+ #phi_mean=pm.Uniform('phi_mean', lower=0,upper=1)
+ #phi_std=pm.Uniform('phi_std', lower=0,upper=1)
+ mu = dict()
+ sd=dict()
+ incidence = dict()
+ incidence_pred=dict()
+ #name_plus=responses_plus[0][0]
+ #observed_plus=responses_plus[0][1]
+
+ #name_minus=responses_minus[0][0]
+ #observed_minus=responses_minus[0][1]
+ #nu[name] = pm.Uniform('nu_'+name, lower=0,upper=30)
+ for name,observed in responses:
+ #std_land=s
+ mu[name] = pm.Normal('mu_'+name, mu=hyper_mu_parameter,sd=hyper_sd_parameter)
+ sd[name] = pm.Exponential('sd_'+name, lam=1/hyper_sd_parameter)
+ #if len(observed_plus)==0:
+ #incidence[name_plus] = pm.StudentT(name_plus,nu=hyper_nu_parameter_plus, mu=mu[name_plus], sigma=sd[name_plus] )
+ if len(observed)!=0:
+ incidence[name] = pm.StudentT(name,nu=hyper_nu_parameter, mu=mu[name], sigma=sd[name] ,observed=observed)
+ incidence_pred[name] = pm.StudentT('incidence_pred'+name,nu=hyper_nu_parameter, mu=mu[name], sigma=sd[name] )
+
+ sample_number=1000
+
+ model_trace = pm.sample(sample_number,target_accept = 0.99)
+ azsum=az.summary(model_trace)
+ azResults.append([incidence_days_number,X,list(azsum.index),list(azsum.columns),azsum.values.tolist()])
+ def prob_responsea_efficient_over_responseb(responsea, responseb):
+ l=[]
+ for i in range(1000):
+ a=model_trace.get_values('incidence_pred'+responsea)
+ np.random.shuffle(a)
+ b=model_trace.get_values('incidence_pred'+responseb)
+ np.random.shuffle(b)
+ l.append(np.float(sum(a < b))/len(a))
+ return l
+ resu=[]
+ from statistics import mean
+ effdis=prob_responsea_efficient_over_responseb(land+"+", land+"-")
+ resu.append([land,[min (effdis),mean(effdis),max(effdis)]])
+ Results.append([incidence_days_number,X,resu])
+ score=Results
+ azscore=azResults
+ with open('Resultsfile7.py', 'w') as f:
+ f.write('score = %s' % score)
+ with open('azResultsfile7.py', 'w') as azf:
+ azf.write('azscore = %s' % azscore)
+