diff --git a/p_1/code/66.py b/p_1/code/66.py
deleted file mode 100644
index a3cd51f2ea3be5bbad91787ef63105c0a5dc3e6f..0000000000000000000000000000000000000000
--- a/p_1/code/66.py
+++ /dev/null
@@ -1,243 +0,0 @@
-###******************************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)
-