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Spectral-Ranking-for-Abnormality

Implementation of the Spectral Ranking for Abnormality (SRA) algorithm as described in the paper K. Nian, H. Zhang, A. Tayal, T. F. Coelman, Y. Li, (2014) 'Unsupervised Spectral Ranking for Anomaly and Application to Auto Insurance Fraud Detection'

##Example1 ######load libraries library(kernlab) library(ggplot2) ######load data data(promotergene) ######transform numeric data to categorical data df=as.data.frame(sapply(promotergene[,-1],catcalinhara)) ######compute hamming distance kernel matrix hammingkernelMatrix = hammingkernel2(df,lambda = .6) ######Perform spectral ranking SpectralAnomaly = sra(hammingkernelMatrix, Xi = .4) ######plot g = ggplot(SpectralAnomaly$EigenSpace,aes(x=np_Eigenvector_1, y = np_Eigenvector_2,color=ifelse(sign(SpectralAnomaly$Anomaly)==-1,1,SpectralAnomaly$Anomaly+1))) + geom_point() + scale_color_gradient("Anomaly",trans="log",low="blue",high="red") g = g + ggtitle(paste("mFLAG= ",SpectralAnomaly$mFLAG)) g = g + theme(legend.title = element_text(face="plain"), legend.text = element_text(color = "white")) g

#Example2 mushroom=read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data", header=FALSE, sep=",") mush.split=split(mushroom,mushroom$V1) mush.e=mush.split[[1]] mush.p=mush.split[[2]] mush.p=mush.p[sample(nrow(mush.p),300),] mushroom =rbind(mush.e,mush.p) ######should have 300 poisonous mushrooms table(mushroom$V1) ######transform numeric data to categorical data df=as.data.frame(sapply(mushroom[,-1],catcalinhara)) ######compute hamming distance kernel matrix ptm <- proc.time() hammingkernelMatrix = hammingkernel(df,lambda = .5) proc.time() - ptm ######Perform spectral ranking ptm <- proc.time() SpectralAnomaly = sra(hammingkernelMatrix, Xi = .1) proc.time() - ptm ######plot g = ggplot(SpectralAnomaly$EigenSpace,aes(x=np_Eigenvector_1, y = np_Eigenvector_2,color=ifelse(sign(SpectralAnomaly$Anomaly)==-1,1,SpectralAnomaly$Anomaly+1))) + geom_point() + scale_color_gradient("Anomaly",trans="log",low="black",high="red") g = g + ggtitle(paste("mFLAG= ",SpectralAnomaly$mFLAG)) g = g + theme(legend.title = element_text(face="plain"), legend.text = element_text(color = "white")) g ######use both first non-principal eigenvectors for the anomaly score g = ggplot(SpectralAnomaly$EigenSpace,aes(x=np_Eigenvector_1, y = np_Eigenvector_2,color=ifelse(sign(SpectralAnomaly$Anomaly)==-1,1,SpectralAnomaly$Anomaly+1+max(abs(SpectralAnomaly$EigenSpace[,"np_Eigenvector_2"])) - abs(SpectralAnomaly$EigenSpace[,"np_Eigenvector_2"])))) + geom_point() + scale_color_gradient("Anomaly",low="black",high="red") g = g + ggtitle(paste("mFLAG= ",SpectralAnomaly$mFLAG)) g = g + theme(legend.title = element_text(face="plain"), legend.text = element_text(color = "white")) g ######Test set AUC library(ROCR) ROCRpred = prediction(SpectralAnomaly$Anomaly, mushroom$V1) perf = performance(ROCRpred, "tpr", "fpr") plot(perf,colorize=T,print.cutoffs.at=seq(0,1,by=0.05),main=paste("AUC: ",as.numeric(performance(ROCRpred, "auc")@y.values)))

#Example3 breastcancer=read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data", header=FALSE, sep=",") ######should have 357 benign cases and 212 malignant table(breastcancer$V2) #transform numeric data to categorical data df=as.data.frame(sapply(breastcancer[,c(-1,-2)],catcalinhara)) ######compute hamming distance kernel matrix ptm <- proc.time() hammingkernelMatrix = hammingkernel(df,lambda = .8) proc.time() - ptm ######Perform spectral ranking ptm <- proc.time() SpectralAnomaly = sra(hammingkernelMatrix, Xi = .4) proc.time() - ptm ######plot g = ggplot(SpectralAnomaly$EigenSpace,aes(x=np_Eigenvector_1, y = np_Eigenvector_2,color=ifelse(sign(SpectralAnomaly$Anomaly)==-1,1,SpectralAnomaly$Anomaly+1))) + geom_point() + scale_color_gradient("Anomaly",trans="log",low="black",high="red") g = g + ggtitle(paste("mFLAG= ",SpectralAnomaly$mFLAG)) g = g + theme(legend.title = element_text(face="plain"), legend.text = element_text(color = "white")) g ######Test set AUC library(ROCR) ROCRpred = prediction(SpectralAnomaly$Anomaly, breastcancer$V2) perf = performance(ROCRpred, "tpr", "fpr") plot(perf,colorize=T,print.cutoffs.at=seq(0,1,by=0.2),main=paste("AUC: ",as.numeric(performance(ROCRpred, "auc")@y.values)))