Implemented kernel PCA in R

Author: tanvirrazin

part_9-dimentionality-reduction/kernel_pca/kernel_pca.R

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+# Importing the dataset
+# ---------------------
+dataset = read.csv('../../data_files/Social_Network_Ads.csv')
+dataset = dataset[, 3:5]
+
+# Splitting the dataset into the Training set and Test set
+# --------------------------------------------------------
+# install.packages('caTools')
+library(caTools)
+set.seed(123)
+split = sample.split(dataset$Purchased, SplitRatio = 0.75)
+training_set = subset(dataset, split==TRUE)
+test_set = subset(dataset, split==FALSE)
+
+# Feature scaling
+# ---------------
+training_set[, 1:2] = scale(training_set[, 1:2])
+test_set[, 1:2] = scale(test_set[, 1:2])
+
+# Applying Kernel PCA
+# -------------------
+# install.packages('kernlab')
+library(kernlab)
+kpca = kpca(~., data=training_set[-3], kernel='rbfdot', features=2)
+training_set_pca = as.data.frame(predict(kpca, training_set))
+training_set_pca$Purchased = training_set$Purchased
+test_set_pca = as.data.frame(predict(kpca, test_set))
+test_set_pca$Purchased = test_set$Purchased
+
+# Fitting Logisitic Regression to training set
+#---------------------------------------------
+classifier = glm(formula = Purchased ~ .,
+                 family = binomial,
+                 data = training_set_pca)
+
+# Predicting the Test set results
+#--------------------------------
+prob_pred = predict(classifier, type='response', newdata=test_set_pca[-3])
+y_pred = ifelse(prob_pred > 0.5, 1, 0)
+
+# Making the Confusion Matrix
+cm = table(test_set_pca[, 3], y_pred)
+
+# Visualizing the Training set results
+#-------------------------------------
+library(ElemStatLearn)
+set = training_set_pca
+X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by=0.01)
+X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by=0.01)
+grid_set = expand.grid(X1, X2)
+colnames(grid_set) = c('V1', 'V2')
+prob_set = predict(classifier, type='response', newdata=grid_set)
+y_grid = ifelse(prob_set > 0.5, 1, 0)
+plot(set[, -3],
+     main='Logistic Regression (Training Set)',
+     xlab='Age', ylab='Estimated Salary',
+     xlim=range(X1), ylim=range(X2))
+contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add=TRUE)
+points(grid_set, pch='.', col=ifelse(y_grid == 1, 'springgreen3', 'tomato'))
+points(set, pch=21, bg=ifelse(set[, 3] == 1, 'green4', 'red3'))
+
+# Visualizing the Test set results
+#---------------------------------
+set = test_set_pca
+X1 = seq(min(set[, 1]) - 1, max(set[, 1] + 1, by=0.01))
+X2 = seq(min(set[, 2]) - 1, max(set[, 2] + 1, by=0.01))
+grid_set = expand.grid(X1, X2)
+colnames(grid_set) = c('V1', 'V2')
+prob_set = predict(classifier, type='response', newdata=grid_set)
+y_grid = ifelse(prob_set > 0.5, 1, 0)
+plot(set[, -3],
+     main='Logistic Regression (Test Set)',
+     xlab='Age', ylab='Estimated Salary',
+     xlim=range(X1), ylim=range(X2))
+contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add=TRUE)
+points(grid_set, pch='.', col=ifelse(y_grid == 1, 'springgreen3', 'tomato'))
+points(set, pch=21, bg=ifelse(set[, 3] == 1, 'green4', 'red3'))