From b8320a8a32ca8f4321b273e63198b8c55d2951c0 Mon Sep 17 00:00:00 2001
From: "Dr. Hamed Khalili" <hamedkhalili@uni-koblenz.de>
Date: Fri, 23 Jun 2023 13:23:38 +0000
Subject: [PATCH] Upload New File
---
p_1/code/tmux_covid_hb.txt | 368 +++++++++++++++++++++++++++++++++++++
1 file changed, 368 insertions(+)
create mode 100644 p_1/code/tmux_covid_hb.txt
diff --git a/p_1/code/tmux_covid_hb.txt b/p_1/code/tmux_covid_hb.txt
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+###******************************HALLO*******************************
+
+
+###***************************BAYESIAN GOVERMENT COVID APPLICATION**************************************************WRITEN BY:
+###********************************************************************************************************HAMED KHALILI***********
+###***************************INPUTS OF THE PROGRAM********************************************************************************************
+import pandas as pd
+import base64
+
+
+azResults=[]
+Results=[]
+countries=['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']#
+for tage in [21]:
+ for massnahme in [['ClosDaycare','ClosDaycarePartial'],['ClosDaycare'],['ClosPrim','ClosPrimPartial'],['ClosPrim'],
+ ['RestaurantsCafes','RestaurantsCafesPartial'],['RestaurantsCafes'],['GymsSportsCentres','GymsSportsCentresPartial'],['GymsSportsCentres'],
+ ['Teleworking','TeleworkingPartial','WorkplaceClosuresPartial','WorkplaceClosures'],['Teleworking','WorkplaceClosures'],
+ ['MasksMandatoryClosedSpacesPartial','MasksMandatoryAllSpaces','MasksMandatoryClosedSpaces','MasksMandatoryAllSpacesPartial'],
+ ['MasksMandatoryAllSpaces','MasksMandatoryClosedSpaces']]:
+ 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 r:
+ return "+"#+str(tr).replace(",", " or")
+
+ return "-"#+str(tr).replace(",", " or")
+ def std():
+ l=[]
+ for idx, name in (enumerate(CBook.iloc[:,5].value_counts().index.tolist())):
+ l.append( [name,CBook.iloc[:,5].value_counts().tolist()[idx],CBook.loc[CBook.iloc[:,5] == name, mass].std()])
+ return l#CBook[str(evaluation_object)+' '+str(evaluation_criteria)+' '+'std'] = CBook.apply(lambda row: add_second_elemant(row[evaluation_object],l), axis=1)
+
+ def mean():
+ l=[]
+ for idx, name in (enumerate(CBook.iloc[:,5].value_counts().index.tolist())):
+ l.append( [name,CBook.iloc[:,5].value_counts().tolist()[idx],CBook.loc[CBook.iloc[:,5] == name, mass].mean()])
+ return l
+ cb =pd.read_excel("cb.xlsx")
+ measures =pd.read_excel("measures.xlsx")
+
+
+
+
+
+ #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['Germany']:
+ land=name
+ cb_2=cb.loc[(cb['countriesAndTerritories'] == land)]
+ cb_2=cb_2[['dateRep','cases']].iloc[::-1]
+ #cb=cb[(cb['dateRep']>=datetime.date(2020,2,3))] # df[(df['date']>datetime.date(2016,1,1))
+ cb_2['dateRep'] = pd.to_datetime(cb_2['dateRep'], format='%d/%m/%Y')
+ #cb_2=cb_2.loc[cb_2['dateRep']>='2020-03-02']
+ cb_2['cases'].values[cb_2['cases']<0] = cb_2['cases'].values[cb_2['cases']<0]*(-1)
+ cb_2['cases'].values[cb_2['cases']==0]=1
+ if cb_2.isnull().values.any():
+ cb_2['cases']=cb_2['cases'].interpolate()
+
+ cb_2=cb_2.reset_index()
+ measures_2=measures.loc[(measures['Country'] == land)]
+ if measures_2.isnull().values.any():
+ #print(measures_2[measures_2['E'].isna()])
+ measures_2=measures_2.dropna()
+ measures_2=measures_2.reset_index()
+ cooBook=cb_2
+ lst=[]
+ cooBook["Rescd"] = [list() for x in range(len(cooBook.index))]
+ measures_2['A'] = pd.to_datetime(measures_2['A'], format='%d/%m/%Y')
+ measures_2['E'] = pd.to_datetime(measures_2['E'], format='%d/%m/%Y')
+ for i in range (0,len(measures_2)):
+ #col="rc"#(measures['Measure'][i])
+ A=(measures_2['A'][i])
+ E=(measures_2['E'][i]++ timedelta(days=1))
+ #if col not in coBook.columns:
+ #coBook[col] = pd.Series(dtype='int')
+
+ for j in range (0,len(cooBook)):
+ date=cooBook['dateRep'][j]
+ #date_E=coBook['time_iso8601'][j-1]
+ #if coBook[col][j] != 1:
+ if date>=A and date<=E:
+ cooBook["Rescd"][j].append(measures_2['Measure'][i])
+ CBook=cooBook
+
+ #CBook['daily_cases']=CBook['daily_cases'].diff()
+ #CBook=CBook[1::]
+ #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)-incidence_days_number):
+ caesdayNplusone=CBook.loc[i+incidence_days_number, ['cases']].to_list()[0]
+ averagecasesdayonetoNminusone=sum(CBook.iloc[i:i+incidence_days_number]['cases'].values)/incidence_days_number
+ #if averagecasesdayonetoNminusone ==0:
+ #print("00000000")
+ CBook.loc[i, ['rpn_minus_one'+str(incidence_days_number)]]=caesdayNplusone/averagecasesdayonetoNminusone-1
+
+
+ #operator="or"
+ mass='rpn_minus_one'+str(incidence_days_number)
+ #CBook[mass]=CBook[mass].pct_change()
+ #CBook=CBook.iloc[1: , :]
+ #CBook[[mass]] = CBook[[mass]].apply(lambda x: 100*x)
+ data_collecting_start_date=min(measures_2['A'])
+ data_collecting_end_date=max(measures_2['E'])
+ if land== 'Hungary':#03/03/2020-11/06/2021 for Hungary
+ data_collecting_start_date='2020-03-11'
+ data_collecting_end_date='2021-06-11'
+ if land== 'Iceland':#23/07/2020-03/12/2021 for
+ data_collecting_start_date='2020-07-23'
+ data_collecting_end_date='2021-12-03'
+ if land== 'Liechtenstein':# 01/10/2020-24/10/2022 for Liechtenstein
+ data_collecting_start_date='2020-10-01'
+ data_collecting_end_date='2022-03-31'
+
+ CBook=CBook.loc[ (CBook['dateRep']>=data_collecting_start_date) ]
+ CBook=CBook.loc[ (CBook['dateRep']<=data_collecting_end_date) ]
+
+
+ CBook[str(X[0][:10]).replace(",", " or")+''+ 'Rescd'] = CBook.apply(lambda row: land+f(row['Rescd'],X), axis=1)
+ #liss.append([land,mean(),std()])
+
+ info ={'country':land,'mean':CBook.iloc[:,4].mean(),'std':CBook.iloc[:,4].std(),'count+':add_elemant(land+'+',mean(),1),'count-':add_elemant(land+'-',mean(),1),'mean+':add_elemant(land+'+',mean(),2),'mean-':add_elemant(land+'-',mean(),2),'std+':add_elemant(land+'+',std(),2),'std-':add_elemant(land+'-',std(),2)}
+ liss_plus = liss_plus.append(info, ignore_index = True)
+ liss_plus=liss_plus.fillna(0)
+ if land in countries:
+
+
+ CBook_plus=CBook[(CBook.iloc[:,5] == land+"+")]
+ CBook_minus=CBook[(CBook.iloc[:,5] == land+"-")]
+ le_plus = preprocessing.LabelEncoder()
+ le_minus = preprocessing.LabelEncoder()
+ rc=str(X[0][:10]).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])
+ #liss
+
+
+
+ #export_excel = liss_plus.to_excel (r"C:\Users\Hamed\Desktop\liss_plus.xlsx")
+ data_mean_positive = np.repeat(liss_plus['mean+'].values.tolist(),liss_plus['count+'].values.tolist())
+ mean_mean_positive=data_mean_positive.mean()
+ mean_std_positive=data_mean_positive.std()
+ data_std_positive = np.repeat(liss_plus['std+'].values.tolist(),liss_plus['count+'].values.tolist())
+ #std_mean_positive=data_std_positive.mean()
+ #std_std_positive=data_std_positive.std()
+ std_min_positive=data_std_positive.min()
+ std_max_positive=data_std_positive.max()
+ data_mean_negative = np.repeat(liss_plus['mean-'].values.tolist(),liss_plus['count-'].values.tolist())
+ mean_mean_negative=data_mean_negative.mean()
+ mean_std_negative=data_mean_negative.std()
+ data_std_negative = np.repeat(liss_plus['std-'].values.tolist(),liss_plus['count-'].values.tolist())
+ #std_mean_negative=data_std_negative.mean()
+ #std_std_negative=data_std_negative.std()
+ std_min_negative=data_std_negative.min()
+ std_max_negative=data_std_negative.max()
+
+ #cb =pd.read_excel(r"C:\Users\Hamed\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Anaconda3 (64-bit)\datac.xlsx")
+ #measures =pd.read_excel(r"C:\Users\Hamed\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Anaconda3 (64-bit)\response_graphs.xlsx")
+
+
+
+ #CBook
+
+
+
+ #mean_mean_positive mean_std_positive std_mean_positive std_std_positive country_mean_positive country_std_positive
+ #uncertainty=0.1
+ #import math
+ with pm.Model() as model:
+ hyper_mu_parameter_positive=pm.Normal('hyper_mu_parameter_positive', mu=mean_mean_positive,sd=mean_std_positive)#
+ hyper_sd_parameter_positive=pm.Uniform('hyper_sd_parameter_positive', lower=std_min_positive,upper=std_max_positive)#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)
+ hyper_mu_parameter_negative=pm.Normal('hyper_mu_parameter_negative', mu=mean_mean_negative,sd=mean_std_negative)#
+ hyper_sd_parameter_negative=pm.Uniform('hyper_sd_parameter_negative', lower=std_min_negative,upper=std_max_negative)
+
+ hyper_nu_parameter_plus=pm.Uniform('hyper_nu_parameter_plus', lower=0,upper=30)
+ hyper_nu_parameter_minus=pm.Uniform('hyper_nu_parameter_minus', 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_plus,observed_plus in responses_plus:
+ std_land=liss_plus.loc[(liss_plus['country'] == name_plus[:-1])].iloc[:,2].to_list()[0]
+ mu[name_plus] = pm.Normal('mu_'+name_plus, mu=hyper_mu_parameter_positive,sd=hyper_sd_parameter_positive)
+ sd[name_plus] = pm.Exponential('sd_'+name_plus, lam=1/std_land)
+ #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_plus)!=0:
+ incidence[name_plus] = pm.StudentT(name_plus,nu=hyper_nu_parameter_plus, mu=mu[name_plus], sigma=sd[name_plus] ,observed=observed_plus)
+ incidence_pred[name_plus] = pm.StudentT('incidence_pred'+name_plus,nu=hyper_nu_parameter_plus, mu=mu[name_plus], sigma=sd[name_plus] )
+ for name_minus,observed_minus in responses_minus:
+ mu[name_minus] = pm.Normal('mu_'+name_minus, mu=hyper_mu_parameter_negative,sd=hyper_sd_parameter_negative)
+ sd[name_minus] = pm.Exponential('sd_'+name_minus, lam=1/std_land)
+ #if len(observed_minus)==0:
+ #incidence[name_minus] = pm.StudentT(name_minus,nu=hyper_nu_parameter_minus, mu=mu[name_minus], sigma=sd[name_minus] )
+ if len(observed_minus)!=0:
+ incidence[name_minus] = pm.StudentT(name_minus,nu=hyper_nu_parameter_minus, mu=mu[name_minus], sigma=sd[name_minus] ,observed=observed_minus)
+ incidence_pred[name_minus] = pm.StudentT('incidence_pred'+name_minus,nu=hyper_nu_parameter_minus, mu=mu[name_minus], sigma=sd[name_minus] )
+ sample_number=1000
+
+ model_trace = pm.sample(sample_number,target_accept = 0.99)#,tune=2000,target_accept = 0.90
+ 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
+
+ #b=50
+
+ #r=[responses_plus,responses_minus]
+ #for i in range(0, len(responses_plus)):
+
+ #if len(responses_plus)==1:
+ #fig, ax = plt.subplots(1,1, figsize=(5, 5))
+ #ax.hist(prob_responsea_efficient_over_responseb(responses_plus[0][0],responses_minus[0][0]), #label="p(+<-)"+responses_plus[0]#[0][:-1])
+ #ax.legend(loc='best')
+ #ax.set_ylabel('frequency')
+ #ax.set_title('efficiency of + compared to -')
+ resu=[]
+ for i in range(0,len(responses_plus)):
+ effdis=prob_responsea_efficient_over_responseb(responses_plus[i][0],responses_minus[i][0])
+ #axs[i].hist(effdis, label="p(+<-)"+responses_plus[i][0][:-1])
+ #axs[i].legend(loc='best')
+ #axs[i].set_ylabel('frequency')
+ #from statistics import mean
+ from statistics import mean
+ #print(mean(effdis)
+ resu.append([responses_plus[i][0][:-1],[min (effdis),mean(effdis),max(effdis)]])
+ #print(resu)
+
+ #print(resu)
+
+ Results.append([incidence_days_number,X,resu])
+
+score=Results
+azscore=azResults
+with open('Resultsfile21.py', 'w') as f:
+ f.write('score = %s' % score)
+with open('azResultsfile21.py', 'w') as azf:
+ azf.write('azscore = %s' % azscore)
+
+
+
+
--
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