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Prediction.py
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import statistics
import numpy as np
class Prediction:
def __init__(self, ilevels, levels, betap, deltaP, penalisation, gradeT):
#The level has the note and the time for each question
#Test data
self.ilevels=ilevels
self.levels=levels
#Initialization of beta distributions for each level
self.betap=betap
self.INLevel=[]
self.NLevel=[]
self.maxGrade=10
self.factor=10
self.deltaPlus=deltaP
self.gradeThreshold=gradeT
self.timePenalisation=penalisation
def Structure(self, base, total, questions):
v1=[]
v2=[]
elem=1
#for i in range(1,150,2):
for i in range(1,total,2):
#print(i," ",dr.tolist()[0+i:2+i])
v1.append(base.tolist()[0+i:2+i])
#if (elem % 15) == 0:
if (elem % questions) == 0:
v2.append(v1)
v1=[]
elem+=1
#In this case, v2 is the last of all rows
return v2
def CalculateGradePenalization(self):
self.ilevels=self.Structure(self.ilevels,30,3)
self.levels=self.Structure(self.levels,150,15)
#Calculate the note with penalization for time used
self.INLevel=[[ilist[0]-((ilist[0])*((ilist[1]/60)*self.timePenalisation)) for ilist in lev] for lev in self.ilevels]
self.NLevel=[[ilist[0]-((ilist[0])*((ilist[1]/60)*self.timePenalisation)) for ilist in lev] for lev in self.levels]
#Generalized Thompson sampling
def Calculate1(self, NLevel):
r=1
maxGrade=10
#Here start the Multi armed bandits for choose the best level. Thompson Sampling...
for i in range(min(len(self.levels[0]),len(self.levels[1]),len(self.levels[2]))):
if i==0:
IRLevel=0
else:
#Take a sample for all calculated posterior distributions
Rlevel=[np.random.beta(self.betap[0][0],self.betap[0][1]),
np.random.beta(self.betap[1][0],self.betap[1][1]),
np.random.beta(self.betap[2][0],self.betap[2][1])]
IRLevel=max(enumerate(Rlevel),key=lambda x: x[1])[0]
print(Rlevel, self.betap)
print("Mean 1: ", self.betap[0][0]/(self.betap[0][0]+self.betap[0][1]))
print("Mean 2: ", self.betap[1][0]/(self.betap[1][0]+self.betap[1][1]))
print("Mean 3: ", self.betap[2][0]/(self.betap[2][0]+self.betap[2][1]))
print(IRLevel,NLevel[IRLevel][i])
#Rewards for succes or not of machine results, here the rewards are inversed because we want to rest into the machine with less success. The reward is normalized value between 0 and 1
#NLevel[IRLevel][i]=NLevel[IRLevel][i]/self.maxGrade
grade=NLevel[IRLevel][i]
deltaMu=(maxGrade-(2*grade))/10
print("DeltaMu: ",deltaMu)
#Change the value of beta parameter index 0
sumAlphaBeta=self.betap[IRLevel][0]+self.betap[IRLevel][1]
self.betap[IRLevel][0]=round((self.betap[IRLevel][0]*r)+(deltaMu*(sumAlphaBeta)),0)
#Control the limits of value for first parameter
self.betap[IRLevel][0]=max(1, self.betap[IRLevel][0])
#Change the value of beta parameter index 1
self.betap[IRLevel][1]=round((sumAlphaBeta*r)-self.betap[IRLevel][0],0)
#Control the limits of value for second parameter
self.betap[IRLevel][1]=max(1, self.betap[IRLevel][1])
print(self.betap[IRLevel][0], self.betap[IRLevel][1])
print(Rlevel, self.betap)
#Bernoulli Thompson Sampling
def UpdateBeta(self, grade, level):
if grade >= self.gradeThreshold:
#Change the value of beta parameter index 1
self.betap[level][1]+=self.deltaPlus
#Correlated Thompson Sampling
if level>0:
self.betap[level-1][1]+=self.deltaPlus/2
if level<len(self.betap)-1:
self.betap[level+1][0]+=self.deltaPlus/2
else:
#Change the value of alpha parameter index 0
self.betap[level][0]+=self.deltaPlus
#Correlated Thompson Sampling
if level>0:
self.betap[level-1][0]+=self.deltaPlus/2
if level<len(self.betap)-1:
self.betap[level+1][1]+=self.deltaPlus/2
def InitializeBeta(self):
c=0
for itemc in self.INLevel:
for i in range(len(itemc)):
#print(itemc[i])
self.UpdateBeta(itemc[i],c)
c+=1
def Calculate(self):
self.InitializeBeta()
NLevel=self.NLevel
file1=open('results_slevel.csv','a+')
file2=open('results_sgrade.csv','a+')
#Here start the Multi armed bandits for choose the best level. Thompson Sampling...
#print("NLevel Vector: ",NLevel)
for i in range(len(NLevel[0])):
#if i==0:
# IRLevel=0
#else:
#Take a sample for all calculated posterior distributions
Rlevel=[np.random.beta(p[0],p[1]) for p in self.betap]
#Take the max probability value
#IRLevel=max(enumerate(Rlevel),key=lambda x: x[1])[0]
IRLevel=max( (v, i) for i, v in enumerate(Rlevel) )[1]
#print(Rlevel, self.betap, [p[0]/(p[0]+p[1]) for p in self.betap])
print("Stochastic ",i," ",IRLevel)
#print(NLevel[IRLevel][i])
file1.write(str(IRLevel)+" ")
file2.write(str(IRLevel)+" "+str(NLevel[IRLevel][i])+" ")
#Rewards for succes or not of machine results, here the rewards are inversed because we want to rest into the machine with less success
self.UpdateBeta(NLevel[IRLevel][i],IRLevel)
file1.write("
")
file2.write("
")
file1.close()
file2.close()
def CalculateSW(self):
file1=open('results_dlevel.csv','a+')
file2=open('results_dgrade.csv','a+')
#file2=open('results_dtm.csv','a+')
IRLevel=0
NIRLevel=0
step=0
mc0=0
mc1=0
mc2=0
mc3=0
mc4=0
#print("INLevel: ",INLevel)
#for i in range(len(NLevel[0])):
Level=self.INLevel
clevel=[0,0,0,0,0]
vindex=[0,0,0,0,0]
for i in range(15):
if i>0:
#print("Value ",Level)
Level[NIRLevel].append(self.NLevel[NIRLevel][vindex[NIRLevel]])
Level[NIRLevel].pop(0)
#print("Value ",Level)
vindex[NIRLevel]+=1
#vindex+=1
mc0=0
mc1=0
mc2=0
mc3=0
mc4=0
mc=0
#print("Level Vector ",Level)
for IRL in range(4):
if IRL == 0:
mc0=Level[IRL][2]+Level[IRL][1]+Level[IRL][0]
mc0=mc0/3
mc0=10*mc0/5
if IRL == 1:
mc1=Level[IRL][2]+Level[IRL][1]+Level[IRL][0]
mc1=mc1/3
if IRL == 2:
mc2=Level[IRL][2]+Level[IRL][1]+Level[IRL][0]
mc2=mc2/3
if IRL == 3:
mc3=Level[IRL][2]+Level[IRL][1]+Level[IRL][0]
mc3=mc3/3
if IRL == 4:
mc4=Level[IRL][2]+Level[IRL][1]+Level[IRL][0]
mc4=mc4/3
#print(mc0," ",mc1," ",mc2,"",mc3)
mc1=max(mc0+((10/5)*mc1), (20/5)*mc1)
mc2=max(mc1+((10/5)*mc2), (30/5)*mc2)
mc3=max(mc2+((10/5)*mc3), (40/5)*mc3)
mc=max(mc3+((10/5)*mc4), (50/5)*mc4)
#print(mc0," ",mc1," ",mc2," ",mc3," ",mc)
#print(mc)
#file2.write(str(mc)+" ")
if mc >= 0 and mc <= 15:
NIRLevel=0
elif mc >= 16 and mc <= 25:
NIRLevel=1
elif mc >= 26 and mc <= 35:
NIRLevel=2
elif mc >= 36 and mc <= 42:
NIRLevel=2
elif mc >= 43 and mc <= 50:
NIRLevel=3
elif mc >= 51 and mc <= 75:
NIRLevel=3
elif mc >= 76 and mc <= 100:
NIRLevel=4
print("Deterministic ",i," ",NIRLevel)
if NIRLevel != IRLevel:
IRLevel=NIRLevel
file1.write(str(IRLevel)+" ")
file2.write(str(IRLevel)+" "+str(self.NLevel[IRLevel][vindex[IRLevel]])+" ")
file1.write("
")
file2.write("
")
file1.close()
file2.close()
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