rm(list = ls())

gc()

library("glmnet")

library("pROC")

library("readxl")

library("nnet")

workbook<-"Feature matrix extracted by BigbiGAN from yout training dataset."

Sta = read_excel(workbook,1)

workbook1<-"Feature matrix extracted by BigbiGAN from yout test dataset."

Sta1 = read_excel(workbook1,1)

workbook2<-"Feature matrix extracted by BigbiGAN from yout validation dataset."

Sta2 = read_excel(workbook2,1)

workbook3<-"Feature matrix extracted by BigbiGAN from yout external validation dataset."

Sta3 = read_excel(workbook3,1)

Train<-data.frame(Sta)

Tval<-data.frame(Sta1)

Test<-data.frame(Sta2)

ExterV<-data.frame(Sta3)

FeaNumTrain<-"Dimension of semantic features extracted by BigBiGAN"

FeaNumTest<-"Dimension of semantic features extracted by BigBiGAN"

FeaNumTval<-"Dimension of semantic features extracted by BigBiGAN"

FeaNumExtV<-"Dimension of semantic features extracted by BigBiGAN"

TrainFea<-data.frame(Train)

TvalFea<-data.frame(Tval)

TestFea<-data.frame(Test)

ExterVFea<-data.frame(ExterV)

Trainy<-Train[(ncol(Train)-FeaNumTrain):(ncol(Train))] ##Features are stored at the end of the matrix.

Trainy<-data.frame(Trainy)

##Linear Classifier

fm <- lm("COVID"~., data=Trainy)

summary(fm)

lmpred_Val<-predict(fm,Tval)

Roc_Val<-roc(Tval$"COVID",lmpred_Val)

auc(Roc_Val)

plot(Roc_Val, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2),

     grid.col=c("green", "red"), max.auc.polygon=TRUE,

     auc.polygon.col="skyblue", print.thres=TRUE)

lmpred_Test<-predict(fm,Test)

Roc_Test<-roc(Test$"COVID",lmpred_Test)

auc(Roc_Test)

plot(Roc_Test, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2),

     grid.col=c("green", "red"), max.auc.polygon=TRUE,

     auc.polygon.col="skyblue", print.thres=TRUE)

lmpred_Ext<-predict(fm,ExterVFea)

Roc_Ext<-roc(ExterVFea$"COVID",lmpred_Ext)

auc(Roc_Ext)

plot(Roc_Ext, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2),

     grid.col=c("green", "red"), max.auc.polygon=TRUE,

     auc.polygon.col="skyblue", print.thres=TRUE)

##Lasso Classifier

y<- Train$COVID

TrainFea_Lasso <- TrainFea

names(TrainFea_Lasso) <- NULL

TrainFea_Lasso<-data.matrix(TrainFea_Lasso)

fit<-glmnet(TrainFea_Lasso,y,alpha=1,family='binomial')

plot(fit, xvar = "lambda", label = TRUE)

cv.fit<-cv.glmnet(TrainFea_Lasso,y,family="binomial")

lmpred_Train <-predict(fit,type="response",newx = TrainFea_Lasso,s=cv.fit$lambda.1se)

Roc_Train<-roc(Train$COVID,lmpred_Train)

auc(Roc_Train)

TvalFea_Lasso <- TvalFea

names(TvalFea_Lasso) <- NULL

TvalFea_Lasso<-data.matrix(TvalFea_Lasso)

lmpred_Tval <-predict(fit,type="response",newx = TvalFea_Lasso,s=cv.fit$lambda.1se)

Roc_Tval<-roc(Tval$COVID,lmpred_Tval)

auc(Roc_Tval)

TestFea_Lasso <- TestFea

names(TestFea_Lasso) <- NULL

TestFea_Lasso<-data.matrix(TestFea_Lasso)

lmpred_Test <-predict(fit,type="response",newx = TestFea_Lasso,s=cv.fit$lambda.1se)

Roc_Test<-roc(Test$COVID,lmpred_Test)

auc(Roc_Test)

ExterFea_Lasso <- ExterVFea

names(ExterFea_Lasso) <- NULL

ExterFea_Lasso<-data.matrix(ExterFea_Lasso)

lmpred_Ext <-predict(fit,type="response",newx = ExterFea_Lasso,s=cv.fit$lambda.min)

Roc_Ext<-roc(ExterV$COVID,lmpred_Ext)

auc(Roc_Ext)