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traintest_H3N2.R
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#function to randomley choose train and test datasets
PrepareDataRemoveOutliers=function(df){
df$numberTipsTrimmed <- as.numeric(df$numberTipsTrimmed)
df$sackin<- as.numeric(df$sackin)
df$colless <- as.numeric(df$colless)
df$Variance <- as.numeric(df$Variance)
df$I2 <- as.numeric(df$I2)
df$B1 <- as.numeric(df$B1)
df$B2<- as.numeric(df$B2)
df$avgLadder <- as.numeric(df$avgLadder)
df$ILnumber <- as.numeric(df$ILnumber)
df$pitchforks <- as.numeric(df$pitchforks)
df$maxHeight <- as.numeric(df$maxHeight)
df$MaxWidth <-as.numeric(df$MaxWidth)
df$DelW <- as.numeric(df$DelW)
df$Stairs1 <- as.numeric(df$Stairs1)
df$Stairs2 <- as.numeric(df$Stairs2)
df$Cherries <- as.numeric(df$Cherries)
df$BS <- as.numeric(df$BS )
df$descinm <- as.numeric(df$descinm)
df$getstattest <- as.numeric(df$getstattest)
df$skewness<- as.numeric(df$skewness)
df$kurtosis <- as.numeric(df$kurtosis )
df$MeanPairwiseDist<- as.numeric(df$MeanPairwiseDist)
df$MaxPairwiseDist<- as.numeric(df$MaxPairwiseDist)
df$diameter<- as.numeric(df$diameter)
df$WienerIndex<- as.numeric(df$WienerIndex)
df$betweenness<- as.numeric(df$betweenness)
df$closeness<- as.numeric(df$closeness)
df$eigenvector <-as.numeric(df$eigenvector)
df$MeadianEp <-as.numeric(df$MeadianEp)
df$MaxEp <-as.numeric(df$MaxEp)
df$MeanEp <-as.numeric(df$MeanEp)
#================================================================================================
#remove the outliers
df_outlier=df[,1:(ncol(df)-1)]
outlier.scores <- lofactor(df_outlier, k=5)
#plot(density(outlier.scores))
# pick top 5 as outliers
outliers <- order(outlier.scores, decreasing=T)[1:5]
# who are outliers
print(outliers)
df=df[-outliers,]
#================================================================================================
df=scaleData(df)
#================================================================================================
#shuffle the data
set.seed(17)
df_cur=df[sample(seq_len(nrow(df)), size = nrow(df)),]
round(length(which(df$Labels==1))/nrow(df),1)
#================================================================================================
#randomley choose 75% of data for train and the rest for test
smp_size <- floor(0.75* nrow(df_cur))
set.seed(123)
train_ind <- sample(seq_len(nrow(df_cur)), size = smp_size)
train<- df_cur[train_ind, ]
test <- df_cur[-train_ind, ]
LTT=list(train=train,test=test)
return(LTT)
}
#================================================================================================
TuneParametersR=function(train,test,kernel){
if(kernel=="linear" | kernel=="radial"){
tune.out=tune(svm, Labels ~ .,
data = train,kernel =kernel, ranges=list(gamma = 2^c(-5:5),cost=2^c(-5:5)),
coef0 =0,degree =3,nu = 0.5,class.weigth=c("0"=0.5,"1"=0.5))
gamma=tune.out$best.parameters$gamma
cost=tune.out$best.parameters$cost
degree = 3
coef0 = 0
svm.fit = svm(data = train, Labels ~ .,
kernel =kernel, degree = 3, gamma = gamma,
coef0 = 0, cost =cost, nu = 0.5, class.weigth=c("0"=0.5,"1"=0.5))
svm.prob <- predict(svm.fit, newdata = test)
agreement <- svm.prob == test$Labels
acc=length(which(svm.prob == test$Labels))/length(test$Labels)
svmmodel.predict<-predict(svm.fit, newdata = test,decision.values=TRUE)
svmmodel.probs<-attr(svmmodel.predict,"decision.values")
svmmodel.class<-predict(svm.fit,test,type="class")
svmmodel.labels<-test$Labels
#roc analysis for test data
svmmodel.prediction<-prediction(svmmodel.probs,svmmodel.labels)
svmmodel.performance<-performance(svmmodel.prediction,"tpr","fpr")
svmmodel.auc<-performance(svmmodel.prediction,"auc")@y.values[[1]]
svmmodel.auc
table(test$Labels,svm.prob)
agreement <- svm.prob == test$Labels
table(agreement)
prop.table(table(agreement))
#computing the true possitive rate
TPR=as.numeric(table(test$Labels,svm.prob)[2,2]/table(test$Labels)[2])
#computing the false negative rate
FNR=1-TPR
#computing the true negative rate
TNR=as.numeric(table(test$Labels,svm.prob)[1,1]/table(test$Labels)[1])
#computing the false positive rate
FPR=1-TNR
r=c(prop.table(table(agreement))[[2]],TNR,TPR,svmmodel.auc,gamma,cost,degree,coef0)
#results_i=round(c(prop.table(table(agreement))[[2]],TPR,FNR,TNR,FPR,svmmodel.auc),3)
}
else{
tune.out=tune(svm, Labels ~ .,
data = train,kernel =kernel, ranges=list(gamma = 2^c(-4:4),cost=2^c(-4:4),degree=c(3,4,5),coef0=c(0,1)),
nu = 0.5,class.weigth=c("0"=0.5,"1"=0.5))
gamma=tune.out$best.parameters$gamma
cost=tune.out$best.parameters$cost
degree=tune.out$best.parameters$degree
coef0=tune.out$best.parameters$coef0
svm.fit = svm(data = train, Labels ~ .,
kernel =kernel, degree = degree, gamma = gamma,
coef0 = coef0, cost =cost, nu = 0.5, class.weigth=c("0"=0.5,"1"=0.5))
svm.prob <- predict(svm.fit, newdata = test)
agreement <- svm.prob == test$Labels
acc=length(which(svm.prob == test$Labels))/length(test$Labels)
svmmodel.predict<-predict(svm.fit, newdata = test,decision.values=TRUE)
svmmodel.probs<-attr(svmmodel.predict,"decision.values")
svmmodel.class<-predict(svm.fit,test,type="class")
svmmodel.labels<-test$Labels
#roc analysis for test data
svmmodel.prediction<-prediction(svmmodel.probs,svmmodel.labels)
svmmodel.performance<-performance(svmmodel.prediction,"tpr","fpr")
svmmodel.auc<-performance(svmmodel.prediction,"auc")@y.values[[1]]
svmmodel.auc
table(test$Labels,svm.prob)
agreement <- svm.prob == test$Labels
table(agreement)
prop.table(table(agreement))
#computing the true possitive rate
TPR=as.numeric(table(test$Labels,svm.prob)[2,2]/table(test$Labels)[2])
#computing the false negative rate
FNR=1-TPR
#computing the true negative rate
TNR=as.numeric(table(test$Labels,svm.prob)[1,1]/table(test$Labels)[1])
#computing the false positive rate
FPR=1-TNR
r=c(prop.table(table(agreement))[[2]],TNR,TPR,svmmodel.auc,gamma,cost,degree,coef0)
#results_i=round(c(prop.table(table(agreement))[[2]],TPR,FNR,TNR,FPR,svmmodel.auc),3)
}
return(r)
}
scaleData=function(data){
scaled.data <- scale(data[,1:(ncol(data)-1)])
scaled.data=as.data.frame(scaled.data)
data$Labels=as.factor(data$Labels)
data=cbind(scaled.data ,data$Labels)
colnames(data)=c("numberTipsTrimmed","sackin",
"colless","Variance","I2","B1","B2","avgLadder","ILnumber","pitchforks",
"maxHeight","MaxWidth","DelW","Stairs1","Stairs2","Cherries","BS","descinm","getstattest","skewness","kurtosis","MeanPairwiseDist","MaxPairwiseDist",
"diameter", "WienerIndex", "betweenness", "closeness", "eigenvector","MeadianEp","MaxEp","MeanEp","Labels")
return(data)
}
RandomFr=function(train,test){
set.seed(17)
rf_model<-train(Labels~.,data=train,method="rf",
trControl=trainControl(method="repeatedcv", number=10, repeats=3),
prox=TRUE,allowParallel=TRUE)
print(rf_model)
print(rf_model$finalModel)
rf.fit=rf_model$finalModel
rf.prob <- predict(rf.fit, newdata = test)
rf.prob.res = summary(rf.prob)
table(test$Labels,rf.prob)
agreement <- rf.prob == test$Labels
table(agreement)
prop.table(table(agreement))
#computing the true possitive rate
TPR=as.numeric(table(test$Labels,rf.prob)[2,2]/table(test$Labels)[2])
#computing the false negative rate
FNR=1-TPR
#computing the true negative rate
TNR=as.numeric(table(test$Labels,rf.prob)[1,1]/table(test$Labels)[1])
#computing the false positive rate
FPR=1-TNR
r=c(prop.table(table(agreement))[[2]],TNR,TPR)
return(r)
}