The Multi-Armed Bandit Problem -Upper Confidence Bound (Ad Campaign)

# Upper Confidence Bound

 # Importing the dataset
dataset = read.csv('Ads_CTR_Optimisation.csv')

 # Implementing UCB

 N = 10000
d = 10
ads_selected = integer(0)
#d is vector has only zero's
numbers_of_selections = integer(d)
sums_of_rewards = integer(d)
total_reward = 0
for (n in 1:N) {
  ad = 0
  max_upper_bound = 0
  for (i in 1:d) {
    if (numbers_of_selections[i] > 0) {
      average_reward = sums_of_rewards[i] / numbers_of_selections[i]
      #upper confidence limit
      delta_i = sqrt(3/2 * log(n) / numbers_of_selections[i])
      upper_bound = average_reward + delta_i
    } else {
        upper_bound = 1e400
    }
    if (upper_bound > max_upper_bound) {
      max_upper_bound = upper_bound
      ad = i
    }
  }
  ads_selected = append(ads_selected, ad)
  numbers_of_selections[ad] = numbers_of_selections[ad] + 1
  reward = dataset[n, ad]
  sums_of_rewards[ad] = sums_of_rewards[ad] + reward
  total_reward = total_reward + reward
}

 # Visualising the results
hist(ads_selected,
     col = 'blue',
     main = 'Histogram of ads selections',
     xlab = 'Ads',
     ylab = 'Number of times each ad was selected')

Comments

Post a Comment

Popular posts from this blog

Apriori

Image
# Data Preprocessing # install.packages('arules') library(arules) dataset = read.csv('Market_Basket_Optimisation.csv', header = FALSE) #sparce matrix dataset = read.transactions('Market_Basket_Optimisation.csv', sep = ',', rm.duplicates = TRUE) distribution of transactions with duplicates: 1  5  summary(dataset) transactions as itemMatrix in sparse format with  7501 rows (elements/itemsets/transactions) and  119 columns (items) and a density of 0.03288973  most frequent items: mineral water          eggs     spaghetti  french fries     chocolate       (Other)           1788          1348          1306          1282          1229         22405  element (itemset/transaction) length distribution: sizes    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   18   19   20  1754 1358 1044  816  667  493  391  324  259  139  102   67   40   22   17    4    1    2    1 
Read more

Decision Tree Classification

Image
# Importing the dataset dataset = read.csv('Social_Network_Ads.csv') dataset = dataset[3:5] # Encoding the target feature as factor dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1)) # Splitting the dataset into the Training set and Test set # install.packages('caTools') library(caTools) set.seed(123) split = sample.split(dataset$Purchased, SplitRatio = 0.75) training_set = subset(dataset, split == TRUE) test_set = subset(dataset, split == FALSE) # Feature Scaling training_set[-3] = scale(training_set[-3]) test_set[-3] = scale(test_set[-3]) # Fitting Decision Tree Classification to the Training set # install.packages('rpart') library(rpart) classifier = rpart(formula = Purchased ~ .,                    data = training_set) # Predicting the Test set results y_pred = predict(classifier, newdata = test_set[-3], type = 'class') # Making the Confusion Matrix cm = table(test
Read more