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Commit ddfbe5d6 authored by Dr. Hamed Khalili's avatar Dr. Hamed Khalili
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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)
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