svm.R

n <- 150 # number of data points
p <- 2 # dimension
sigma <- 1 # variance of the distribution
meanpos <- 0 # centre of the distribution of positive examples
meanneg <- 3 # centre of the distribution of negative examples
npos <- round(n/2) # number of positive examples
nneg <- n-npos # number of negative examples
# Generate the positive and negative examples
xpos <- matrix(rnorm(npos*p,mean=meanpos,sd=sigma),npos,p)
xneg <- matrix(rnorm(nneg*p,mean=meanneg,sd=sigma),npos,p)
x <- rbind(xpos,xneg)
# Generate the labels
y <- matrix(c(rep(1,npos),rep(-1,nneg)))
# Visualize the data
plot(x,col=ifelse(y>0,1,2))
legend("topleft",c('Positive','Negative'),col=seq(2),pch=1,text.col=seq(2))

## Prepare a training and a test set ##
ntrain <- round(n*0.8) # number of training examples
tindex <- sample(n,ntrain) # indices of training samples
xtrain <- x[tindex,]
xtest <- x[-tindex,]
ytrain <- y[tindex]
ytest <- y[-tindex]
istrain=rep(0,n)
istrain[tindex]=1

# Visualize
plot(x,col=ifelse(y>0,1,2),pch=ifelse(istrain==1,1,2))
legend("topleft",c('Positive Train','Positive Test','Negative Train','Negative Test'),
       col=c(1,1,2,2),pch=c(1,2,1,2),text.col=c(1,1,2,2))

# load the kernlab package
library(kernlab)
# train the SVM
svp <- ksvm(xtrain,ytrain,type="C-svc",kernel='vanilladot',C=100,scaled=c())
svp
# Attributes that you can access
attributes(svp)
# For example, the support vectors
alpha(svp)
alphaindex(svp)
b(svp)
# Use the built-in function to pretty-plot the classifier
plot(svp,data=xtrain)

# Predict labels on test
ypred = predict(svp,xtest)
table(ytest,ypred)
# Compute accuracy
sum(ypred==ytest)/length(ytest)
# Compute at the prediction scores
ypredscore = predict(svp,xtest,type="decision")
# Check that the predicted labels are the signs of the scores
table(ypredscore > 0,ypred)
# Package to compute ROC curve, precision-recall etc...
library(ROCR)
pred <- prediction(ypredscore,ytest)
# Plot ROC curve
perf <- performance(pred, measure = "tpr", x.measure = "fpr")
plot(perf)
# Plot precision/recall curve
perf <- performance(pred, measure = "prec", x.measure = "rec")
plot(perf)
# Plot accuracy as function of threshold
perf <- performance(pred, measure = "acc")
plot(perf)