.Rhistory

dataset = read.csv("Ads_CTR_Optimisation")
setwd("~/Desktop/Machine_Learning_Algorithms_R/Part 6 - Reinforcement Learning/Section 33 - Thompson Sampling")
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Thompson Sampling
# Data preprocessing
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Implementing Thompson Sampling
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
for(n in 1:N){
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d){
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random){
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1){
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
# Visualise Thompson Sampling
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
for(n in 1:N){
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d){
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random){
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1){
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
reward = dataset[n, ad]
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
for(n in 1:N){
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d){
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random){
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1){
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
ad = 0
max_random = 0
for(n in 1:N){
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d){
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random){
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1){
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
reward = dataset[n, ad]
for(n in 1:N) {
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d) {
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random) {
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1) {
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
reward = dataset[n, ad]
# Thompson Sampling
# Data preprocessing
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Implementing Thompson Sampling
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
for(n in 1:N) {
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d) {
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random) {
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1) {
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
# Visualise Thompson Sampling
# Thompson Sampling
# Data preprocessing
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Implementing Thompson Sampling
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
for(n in 1:N) {
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d) {
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random) {
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1) {
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
# Visualise Thompson Sampling
random_beta = rbeta(n = 1 # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
# Thompson Sampling
# Data preprocessing
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Implementing Thompson Sampling
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
for(n in 1:N) {
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d) {
random_beta = rbeta(n = 1, # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random) {
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1) {
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
# Visualise Thompson Sampling
# Thompson Sampling
# Data preprocessing
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Implementing Thompson Sampling
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
for(n in 1:N) {
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d) {
random_beta = rbeta(n = 1, # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random) {
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1) {
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
}
# Visualise Thompson Sampling
# Thompson Sampling
# Data preprocessing
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Implementing Thompson Sampling
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
total_reward = 0
for(n in 1:N) {
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d) {
random_beta = rbeta(n = 1, # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random) {
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1) {
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
total_reward = total_reward + reward
}
# Visualise Thompson Sampling
# Thompson Sampling
# Data preprocessing
dataset = read.csv("Ads_CTR_Optimisation.csv")
# Implementing Thompson Sampling
N = 10000 # number of rounds
d = 10 # number of ads
ads_selected = integer(0)
# for each ad - containing the number of times it got the reward 1 or 0 (for each ad -> vector)
numbers_of_rewards_1 = integer(d)
numbers_of_rewards_0 = integer(d)
total_reward = 0
for(n in 1:N) {
ad = 0 # ad selected at specific round
max_random = 0 # maximum random draw
for(i in 1:d) {
random_beta = rbeta(n = 1, # number of observations - we want one random draw
shape1 = numbers_of_rewards_1[i] + 1,
shape2 = numbers_of_rewards_0[i] + 1) # random draws from the beta distribution
if(random_beta > max_random) {
max_random = random_beta
ad = i
}
}
ads_selected = append(ads_selected, ad)
reward = dataset[n, ad]
if(reward == 1) {
# number of rewards for selected ad
numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1
} else {
numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1
}
total_reward = total_reward + reward
}
# Visualise Thompson Sampling
hist(ads_selected)
hist(ads_selected,
color = "light_blue")
hist(ads_selected,
color = "blue")
# Visualise Thompson Sampling
hist(ads_selected,
col = "blue")
hist(ads_selected,
col = "light_blue")
hist(ads_selected,
col = "light blue")
hist(ads_selected,
col = "light blue",
main = "Histogram of Selected Ads by Thompson Sampling",
xlab = "Ads",
ylab = "Frequency of Ad Selection")
hist(ads_selected,
col = "light blue",
main = "Histogram of ads selection for Thompson Sampling",
xlab = "Ads",
ylab = "Number of times each ad was selected")