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FigureS2.py
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import matplotlib.pyplot as plt
import numpy as np
import pickle
if __name__ == '__main__':
Figure = plt.figure(figsize=(15.36,8.96))
plt.subplot(2,3,1)
with open('Data/PreCOVID-Clearance/PreCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
NoMean = []
RandMean = []
RandStd = []
DegMean = []
DegStd = []
ShorrMean = []
ShorrStd = []
GreedyMean = []
GreedyStd = []
for k in range(500,5000,500):
NoMean.append(np.mean(np.sum(loads_other_no[int((k-500)/500),:,:],axis=1)))
RandMean.append(np.mean(np.sum(loads_other_rand[int((k-500)/500),:,:],axis=1)))
RandStd.append(np.std(np.sum(loads_other_rand[int((k-500)/500),:,:],axis=1)))
DegMean.append(np.mean(np.sum(loads_other_deg[int((k-500)/500),:,:],axis=1)))
DegStd.append(np.std(np.sum(loads_other_deg[int((k-500)/500),:,:],axis=1)))
ShorrMean.append(np.mean(np.sum(loads_other_shorr[int((k-500)/500),:,:],axis=1)))
ShorrStd.append(np.std(np.sum(loads_other_shorr[int((k-500)/500),:,:],axis=1)))
GreedyMean.append(np.mean(np.sum(loads_other_greedy[int((k-500)/500),:,:],axis=1)))
GreedyStd.append(np.std(np.sum(loads_other_greedy[int((k-500)/500),:,:],axis=1)))
RandMean = np.array(RandMean)/np.array(NoMean)
RandStd = np.array(RandStd)/np.array(NoMean)
DegMean = np.array(DegMean)/np.array(NoMean)
DegStd = np.array(DegStd)/np.array(NoMean)
ShorrMean = np.array(ShorrMean)/np.array(NoMean)
ShorrStd = np.array(ShorrStd)/np.array(NoMean)
GreedyMean = np.array(GreedyMean)/np.array(NoMean)
GreedyStd = np.array(GreedyStd)/np.array(NoMean)
x1 = np.array(list(range(9))) + 1 - 0.3
x2 = np.array(list(range(9))) + 1 - 0.1
x3 = np.array(list(range(9))) + 1 + 0.1
x4 = np.array(list(range(9))) + 1 + 0.3
plt.bar(x1,RandMean,yerr=RandStd,capsize=4,color='C7',label='Random',width=0.2)
plt.bar(x2,DegMean,yerr=DegStd,capsize=4,color='C0',label='Degree',width=0.2)
plt.bar(x3,ShorrMean,yerr=ShorrStd,capsize=4,color='#6B8E23',label='Shorr',width=0.2)
plt.bar(x4,GreedyMean,yerr=GreedyStd,capsize=4, color='C3',label='Greedy-Spectral',width=0.2)
plt.xlabel(r"Budget $k$", fontsize=18)
plt.ylabel(r"$\gamma_{Loads}$", fontsize=18)
plt.hlines(1,0.5,10.5,linewidth=1,color='black',linestyle='dashed')
plt.xticks([1,2,3,4,5,6,7,8,9],[500,1000,1500,2000,2500,3000,3500,4000,4500],fontsize=10)
plt.yticks([0,0.2,0.4,0.6,0.8,1.0],[0,0.2,0.4,0.6,0.8,1.0],fontsize=12)
plt.xlim(0.5,9.5)
plt.ylim(bottom=0,top=1.1)
plt.title('UVA-PreCOVID',fontsize=14)
plt.legend(loc=2,fontsize=14)
plt.subplot(2,3,2)
with open('Data/PreCOVID-Clearance/PreCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
NoMean = []
RandMean = []
RandStd = []
DegMean = []
DegStd = []
ShorrMean = []
ShorrStd = []
GreedyMean = []
GreedyStd = []
for k in range(500,5000,500):
NoMean.append(np.mean(cum_new_cases_no[int((k-500)/500),:,-1]))
RandMean.append(np.mean(cum_new_cases_rand[int((k-500)/500),:,-1]))
RandStd.append(np.std(cum_new_cases_rand[int((k-500)/500),:,-1]))
DegMean.append(np.mean(cum_new_cases_deg[int((k-500)/500),:,-1]))
DegStd.append(np.std(cum_new_cases_deg[int((k-500)/500),:,-1]))
ShorrMean.append(np.mean(cum_new_cases_shorr[int((k-500)/500),:,-1]))
ShorrStd.append(np.std(cum_new_cases_shorr[int((k-500)/500),:,-1]))
GreedyMean.append(np.mean(cum_new_cases_greedy[int((k-500)/500),:,-1]))
GreedyStd.append(np.std(cum_new_cases_greedy[int((k-500)/500),:,-1]))
RandMean = np.array(RandMean)/np.array(NoMean)
RandStd = np.array(RandStd)/np.array(NoMean)
DegMean = np.array(DegMean)/np.array(NoMean)
DegStd = np.array(DegStd)/np.array(NoMean)
ShorrMean = np.array(ShorrMean)/np.array(NoMean)
ShorrStd = np.array(ShorrStd)/np.array(NoMean)
GreedyMean = np.array(GreedyMean)/np.array(NoMean)
GreedyStd = np.array(GreedyStd)/np.array(NoMean)
x1 = np.array(list(range(9))) + 1 - 0.3
x2 = np.array(list(range(9))) + 1 - 0.1
x3 = np.array(list(range(9))) + 1 + 0.1
x4 = np.array(list(range(9))) + 1 + 0.3
plt.bar(x1,RandMean,yerr=RandStd,capsize=4,color='C7',label='Random',width=0.2)
plt.bar(x2,DegMean,yerr=DegStd,capsize=4,color='C0',label='Degree',width=0.2)
plt.bar(x3,ShorrMean,yerr=ShorrStd,capsize=4,color='#6B8E23',label='Shorr',width=0.2)
plt.bar(x4,GreedyMean,yerr=GreedyStd,capsize=4, color='C3',label='Greedy-Spectral',width=0.2)
plt.xlabel(r"Budget $k$", fontsize=18)
plt.ylabel(r"$\gamma_{Cases}$", fontsize=18)
plt.hlines(1,0.5,10.5,linewidth=1,color='black',linestyle='dashed')
plt.xticks([1,2,3,4,5,6,7,8,9],[500,1000,1500,2000,2500,3000,3500,4000,4500],fontsize=10)
plt.yticks([0,0.2,0.4,0.6,0.8,1.0],[0,0.2,0.4,0.6,0.8,1.0],fontsize=12)
plt.xlim(0.5,9.5)
plt.ylim(bottom=0,top=1.1)
plt.title('UVA-PreCOVID',fontsize=14)
#plt.legend(loc=1,fontsize=14)
plt.subplot(2,3,3)
with open('Data/PreCOVID-Clearance/PreCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
Start = 40
End = 140
ThresholdList = range(Start,End+1,1)
XValue = []
XLabel = []
for X in range(Start,End+1,10):
XValue.append((X-Start))
XLabel.append(str(X))
for k in [4000]:
Rand = []
Deg = []
Shorr = []
Greedy = []
with open('Data/PreCOVID-Clearance/PreCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
for threshold in ThresholdList:
Rand.append(len(np.where(cum_new_cases_rand[int((k-500)/500),:,-1]>threshold)[0]))
Deg.append(len(np.where(cum_new_cases_deg[int((k-500)/500),:,-1]>threshold)[0]))
Shorr.append(len(np.where(cum_new_cases_shorr[int((k-500)/500),:,-1]>threshold)[0]))
Greedy.append(len(np.where(cum_new_cases_greedy[int((k-500)/500),:,-1]>threshold)[0]))
X = np.array(list(range(len(ThresholdList)))) + 1
plt.plot(X, Rand, '-', label='Random', linewidth=2, color='C7')
plt.plot(X, Deg, '-', label='Degree', linewidth=2, color='C0')
plt.plot(X, Shorr, '-', label='Shorr Score', linewidth=2, color='#6B8E23')
plt.plot(X, Greedy, '-', label='Greedy-Spectral', linewidth=2, color='C3')
plt.xlabel("Target", fontsize=18)
plt.ylabel(r"$Prob[Cases > Target]$", fontsize=18)
plt.xticks(XValue,XLabel,fontsize=12)
plt.xlim(0,100)
plt.yticks([0,200,400,600,800,1000],['0','0.2','0.4','0.6','0.8','1.0'],fontsize=12)
plt.ylim(bottom=0,top=1000)
plt.title('UVA-PreCOVID: Budget k='+str(k),fontsize=14)
plt.subplot(2,3,4)
with open('Data/PostCOVID-Clearance/PostCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
NoMean = []
RandMean = []
RandStd = []
DegMean = []
DegStd = []
ShorrMean = []
ShorrStd = []
GreedyMean = []
GreedyStd = []
for k in range(500,5000,500):
NoMean.append(np.mean(np.sum(loads_other_no[int((k-500)/500),:,:],axis=1)))
RandMean.append(np.mean(np.sum(loads_other_rand[int((k-500)/500),:,:],axis=1)))
RandStd.append(np.std(np.sum(loads_other_rand[int((k-500)/500),:,:],axis=1)))
DegMean.append(np.mean(np.sum(loads_other_deg[int((k-500)/500),:,:],axis=1)))
DegStd.append(np.std(np.sum(loads_other_deg[int((k-500)/500),:,:],axis=1)))
ShorrMean.append(np.mean(np.sum(loads_other_shorr[int((k-500)/500),:,:],axis=1)))
ShorrStd.append(np.std(np.sum(loads_other_shorr[int((k-500)/500),:,:],axis=1)))
GreedyMean.append(np.mean(np.sum(loads_other_greedy[int((k-500)/500),:,:],axis=1)))
GreedyStd.append(np.std(np.sum(loads_other_greedy[int((k-500)/500),:,:],axis=1)))
RandMean = np.array(RandMean)/np.array(NoMean)
RandStd = np.array(RandStd)/np.array(NoMean)
DegMean = np.array(DegMean)/np.array(NoMean)
DegStd = np.array(DegStd)/np.array(NoMean)
ShorrMean = np.array(ShorrMean)/np.array(NoMean)
ShorrStd = np.array(ShorrStd)/np.array(NoMean)
GreedyMean = np.array(GreedyMean)/np.array(NoMean)
GreedyStd = np.array(GreedyStd)/np.array(NoMean)
x1 = np.array(list(range(9))) + 1 - 0.3
x2 = np.array(list(range(9))) + 1 - 0.1
x3 = np.array(list(range(9))) + 1 + 0.1
x4 = np.array(list(range(9))) + 1 + 0.3
plt.bar(x1,RandMean,yerr=RandStd,capsize=4,color='C7',label='Random',width=0.2)
plt.bar(x2,DegMean,yerr=DegStd,capsize=4,color='C0',label='Degree',width=0.2)
plt.bar(x3,ShorrMean,yerr=ShorrStd,capsize=4,color='#6B8E23',label='Shorr',width=0.2)
plt.bar(x4,GreedyMean,yerr=GreedyStd,capsize=4, color='C3',label='Greedy-Spectral',width=0.2)
plt.xlabel(r"Budget $k$"+"\n\n(a) "+r"$\gamma_{Loads}$", fontsize=18)
plt.ylabel(r"$\gamma_{Loads}$", fontsize=18)
plt.hlines(1,0.5,10.5,linewidth=1,color='black',linestyle='dashed')
plt.xticks([1,2,3,4,5,6,7,8,9],[500,1000,1500,2000,2500,3000,3500,4000,4500],fontsize=10)
plt.yticks([0,0.2,0.4,0.6,0.8,1.0],[0,0.2,0.4,0.6,0.8,1.0],fontsize=12)
plt.xlim(0.5,9.5)
plt.ylim(bottom=0,top=1.1)
plt.title('UVA-PostCOVID',fontsize=14)
plt.legend(loc=2,fontsize=14)
plt.subplot(2,3,5)
with open('Data/PostCOVID-Clearance/PostCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
NoMean = []
RandMean = []
RandStd = []
DegMean = []
DegStd = []
ShorrMean = []
ShorrStd = []
GreedyMean = []
GreedyStd = []
for k in range(500,5000,500):
NoMean.append(np.mean(cum_new_cases_no[int((k-500)/500),:,-1]))
RandMean.append(np.mean(cum_new_cases_rand[int((k-500)/500),:,-1]))
RandStd.append(np.std(cum_new_cases_rand[int((k-500)/500),:,-1]))
DegMean.append(np.mean(cum_new_cases_deg[int((k-500)/500),:,-1]))
DegStd.append(np.std(cum_new_cases_deg[int((k-500)/500),:,-1]))
ShorrMean.append(np.mean(cum_new_cases_shorr[int((k-500)/500),:,-1]))
ShorrStd.append(np.std(cum_new_cases_shorr[int((k-500)/500),:,-1]))
GreedyMean.append(np.mean(cum_new_cases_greedy[int((k-500)/500),:,-1]))
GreedyStd.append(np.std(cum_new_cases_greedy[int((k-500)/500),:,-1]))
RandMean = np.array(RandMean)/np.array(NoMean)
RandStd = np.array(RandStd)/np.array(NoMean)
DegMean = np.array(DegMean)/np.array(NoMean)
DegStd = np.array(DegStd)/np.array(NoMean)
ShorrMean = np.array(ShorrMean)/np.array(NoMean)
ShorrStd = np.array(ShorrStd)/np.array(NoMean)
GreedyMean = np.array(GreedyMean)/np.array(NoMean)
GreedyStd = np.array(GreedyStd)/np.array(NoMean)
x1 = np.array(list(range(9))) + 1 - 0.3
x2 = np.array(list(range(9))) + 1 - 0.1
x3 = np.array(list(range(9))) + 1 + 0.1
x4 = np.array(list(range(9))) + 1 + 0.3
plt.bar(x1,RandMean,yerr=RandStd,capsize=4,color='C7',label='Random',width=0.2)
plt.bar(x2,DegMean,yerr=DegStd,capsize=4,color='C0',label='Degree',width=0.2)
plt.bar(x3,ShorrMean,yerr=ShorrStd,capsize=4,color='#6B8E23',label='Shorr',width=0.2)
plt.bar(x4,GreedyMean,yerr=GreedyStd,capsize=4, color='C3',label='Greedy-Spectral',width=0.2)
plt.xlabel(r"Budget $k$"+"\n\n(b) "+r"$\gamma_{Cases}$", fontsize=18)
plt.ylabel(r"$\gamma_{Cases}$", fontsize=18)
plt.hlines(1,0.5,10.5,linewidth=1,color='black',linestyle='dashed')
plt.xticks([1,2,3,4,5,6,7,8,9],[500,1000,1500,2000,2500,3000,3500,4000,4500],fontsize=10)
plt.yticks([0,0.2,0.4,0.6,0.8,1.0],[0,0.2,0.4,0.6,0.8,1.0],fontsize=12)
plt.xlim(0.5,9.5)
plt.ylim(bottom=0,top=1.1)
plt.title('UVA-PostCOVID',fontsize=14)
#plt.legend(loc=1,fontsize=14)
plt.subplot(2,3,6)
with open('Data/PostCOVID-Clearance/PostCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
Start = 40
End = 140
ThresholdList = range(Start,End+1,1)
XValue = []
XLabel = []
for X in range(Start,End+1,10):
XValue.append((X-Start))
XLabel.append(str(X))
for k in [4000]:
Rand = []
Deg = []
Shorr = []
Greedy = []
with open('Data/PostCOVID-Clearance/PostCOVID-Clearance-0.pkl', 'rb') as pkl:
cum_new_cases_no, cum_new_cases_rand, cum_new_cases_deg, cum_new_cases_greedy, cum_new_cases_shorr, loads_other_no, loads_other_rand, loads_other_deg, loads_other_greedy, loads_other_shorr, rhos_rand, rhos_deg, rhos_greedy, rhos_shorr = pickle.load(pkl)
for threshold in ThresholdList:
Rand.append(len(np.where(cum_new_cases_rand[int((k-500)/500),:,-1]>threshold)[0]))
Deg.append(len(np.where(cum_new_cases_deg[int((k-500)/500),:,-1]>threshold)[0]))
Shorr.append(len(np.where(cum_new_cases_shorr[int((k-500)/500),:,-1]>threshold)[0]))
Greedy.append(len(np.where(cum_new_cases_greedy[int((k-500)/500),:,-1]>threshold)[0]))
X = np.array(list(range(len(ThresholdList)))) + 1
plt.plot(X, Rand, '-', label='Random', linewidth=2, color='C7')
plt.plot(X, Deg, '-', label='Degree', linewidth=2, color='C0')
plt.plot(X, Shorr, '-', label='Shorr Score', linewidth=2, color='#6B8E23')
plt.plot(X, Greedy, '-', label='Greedy-Spectral', linewidth=2, color='C3')
plt.xlabel("Target\n\n(c) "+r"$Prob[Cases > Target]$", fontsize=18)
plt.ylabel(r"$Prob[Cases > Target]$", fontsize=18)
plt.xticks(XValue,XLabel,fontsize=12)
plt.xlim(0,100)
plt.yticks([0,200,400,600,800,1000],['0','0.2','0.4','0.6','0.8','1.0'],fontsize=12)
plt.ylim(bottom=0,top=1000)
plt.title('UVA-PostCOVID: Budget k='+str(k),fontsize=14)
plt.tight_layout()
plt.savefig('FigureS2.pdf')