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""" This code probides a basic skeleton for the stationary bandit code. It should be adapted by the students for their work. """ import csv, random class BanditSet(object): """ This object represents...

1 answer below »
"""
This code probides a basic skeleton for the
stationary bandit code. It should be adapted
y the students for their work.
"""
import csv, random
class BanditSet(object):
"""
This object represents a set of arms for a stationary multi-armed
bandit problem it will store a fixed set of arms from a set and
will then maintain them over multiple iterations.
"""

def __init__(self, DataRows, ArmNames, ExpRate,
XXXXXXXXXXDistribParam, DecayRate, RewardWeight):
"""
XXXXXXXXXXThis initializes the set of choices by acting as a factory
XXXXXXXXXXclass to create one arm instance for each of the choices.
XXXXXXXXXXThe names and the rows will come from the file that
XXXXXXXXXXis read in.
"""
# Store the Data for later use.
XXXXXXXXXXself.Data = DataRows

# Initialize the parameters.
XXXXXXXXXXself.ExplorationRate = ExpRate
XXXXXXXXXXself.DistributionParameter = DistribParam
XXXXXXXXXXself.DecayRate = DecayRate
XXXXXXXXXXself.RewardWeight = RewardWeight
# Store items for each of the arms.
XXXXXXXXXXself.Names = ArmNames
# Store a list for the weights.
XXXXXXXXXXself.Weights = [-1 for I in range(len(ArmNames))]
# Calculate the starting probability and add it.
XXXXXXXXXXStartProb = 1 / float(len(ArmNames))
XXXXXXXXXXself.Probabilities = [StartProb for I in range(len(ArmNames))]
# And store the Cumulative Reward
XXXXXXXXXXself.CumulativeReward = 0
def handleRows(self):
"""
XXXXXXXXXXProcess each of the rows and update our running reward
XXXXXXXXXXand the basic probabilies for each one.
"""
# We initialize the cumulative
# Reward to be 0
XXXXXXXXXXself.CumulativeReward = 0
# Now iterate over the rows and make each
# of the choices.
XXXXXXXXXXfor Cu
Row in self.Rows:
# Now pick one from the list of probabilities.
# Get the reward value from the row.
# Update the reward weight.
# And update the probabilities.
# Return the cumulative reward.
XXXXXXXXXXreturn(self.CumulativeReward)

def pickArmIndex(self):
"""
XXXXXXXXXXPick an index based upon the probabilities
XXXXXXXXXXusing the cumulative score approach based
XXXXXXXXXXupon a random value.
"""
XXXXXXXXXXpass
def getReward(self, Index):
"""
XXXXXXXXXXUse the Armnames to get the reward for the
XXXXXXXXXXchosen arm.
"""
XXXXXXXXXXpass
def updateWeight(self, Index, Reward):
"""
XXXXXXXXXXUpdate the weight for the chosen index using
XXXXXXXXXXthe parameters.
"""
XXXXXXXXXXpass
def updateProbability(self, Index):
"""
XXXXXXXXXXUpdate the probability for the index from its weight.
"""
XXXXXXXXXXpass
def normalizeProbabilities(self, Index, Reward):
"""
XXXXXXXXXXNormalize the probability values.
"""
XXXXXXXXXXpass

"Sample A","Sample B","Sample C","Sample D"
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0
assignment3-4z5odfg2.png banditskeleton-1-5rwsgans.py banditsdata-3-vhh4m3mz.csv
Answered Same Day Aug 02, 2021

Solution

Swapnil answered on Aug 03 2021
146 Votes
89040/Bandits.py
import csv, random, sys

class BanditSet(object):
def __init__(self, DataRows, ArmNames, ExpRate, DistribParam, DecayRate, RewardWeight):

self.Data = DataRows
self.ExplorationRate = ExpRate
self.DistributionParameter = DistribParam
self.DecayRate = DecayRate
self.RewardWeight = RewardWeight
self.Names = ArmNames
StartProb = 1 / float(len(ArmNames))
self.Probabilities = [StartProb for I in range(len(ArmNames))]
self.Weights = [StartProb for I in range(len(ArmNames))]
self.CumulativeReward = 0
def handleRows(self):
self.CumulativeReward = 0

for Cu
Row in self.Data:
print(Cu
Row)
reward_arm_index = self.pickArmIndex()
rewardValue = self.getReward(reward_arm_index,Cu
Row)
self.updateWeight(reward_arm_index,rewardValue)
self.updateProbability(reward_arm_index)
self.normalizeProbabilities(reward_arm_index,rewardValue)
self.CumulativeReward = float(self.CumulativeReward)+float(rewardValue)
print('choice made: ',reward_arm_index)
print('reward from choice: ',rewardValue)
print('cumulative reward: ',self.CumulativeReward)
return(self.CumulativeReward)

def pickArmIndex(self):
return random.choices(range(len(self.Probabilities)), weights=self.Probabilities, k=1)[0]

def getReward(self, Index,row):
choosenArm = self.Names[Index]
return row[choosenArm]

def updateWeight(self, Index, Reward):
weight = float(self.DecayRate)*float(self.Weights[Index])+float(self.RewardWeight)*float(Reward)
totalWeight = 0
self.Weights[Index]=weight

for cu
entweight in self.Weights:
totalWeight = totalWeight+ cu
entweight
names = self.Names
for weightIndex in range(len(self.Weights)):
weightIndex
self.Weights[weightIndex] =float(self.Weights[weightIndex])/float(totalWeight)
def updateProbability(self, Index):
probability = float(self.Weights[Index]) * float(1-float((self.ExplorationRate)))+ float(self.ExplorationRate)*float(self.DistributionParameter)
self.Probabilities[Index]= probability
pass
def normalizeProbabilities(self, Index, Reward):
totalProbability=0

for probability in self.Probabilities:
totalProbability = totalProbability+ probability

for probabilityIndex in range(len(self.Probabilities)):
self.Probabilities[probabilityIndex] =float(self.Probabilities[probabilityIndex])/float(totalProbability)
def main():
fileName = sys.argv[1]
DataRows =[]
ExpRate= .3
DistribParam=.1
DecayRate=.6
RewardWeight=.9

with open(fileName, newline='') as csvfile:
reader = csv.DictReader(csvfile)
ArmNames = reader.fieldnames

for row in reader:
DataRows.append(row)
bandits = BanditSet(DataRows, ArmNames, ExpRate, DistribParam, DecayRate, RewardWeight)
bandits.handleRows();
main()
89040/BanditsData.csv
"Sample A","Sample B","Sample C","Sample...
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