diff --git a/p_1/code/tmux_covid_hb.txt b/p_1/code/tmux_covid_hb.txt
deleted file mode 100644
index ca6fd5093c23aece8890311c0209a776e041a7ab..0000000000000000000000000000000000000000
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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)
-
-
-
-