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models.R
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models.R
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suppressPackageStartupMessages(require(caret))
suppressPackageStartupMessages(require(rpart))
suppressPackageStartupMessages(require(tidyverse))
suppressPackageStartupMessages(require(rpart.plot) )
suppressPackageStartupMessages(require(RWeka))
suppressPackageStartupMessages(require(pROC))
suppressPackageStartupMessages(require(ROCR))
suppressPackageStartupMessages(require(kernlab))
suppressPackageStartupMessages(require(e1071))
suppressPackageStartupMessages(require(rminer))
"
Dans ce fichier nous définissons tout nos modèles que l'on souhaite rendre disponible sur notre plateforme
- Premièrement on créé notre base d'apprentissage et test sur les données d'entrées seon le split voulu par le user si le nombre d'observation est suppérieur à 150
- Puis on crée la vriable cible sur la base du choix de l'utilisateur en tenant compte de l'input de la label positive.
- On crée un dataframe contenant les contribution de chaque variable sur les modèles. Seules les combinaison de variables sont tenues en compte.
- Un dataframe aussi est créé pour stocké les probabilité d'appartenance d'une observation dans la classe positive.
Ce dataframe est ensuite utilisé pour tracer les courbes de ROC associés.
"
testModel = function(data,dataT,i_model,grpeParams,grpeNewCat,positiveLabel,trCtr,negative_label) {
if(i_model %in% c('glm','nb','lda','knn','cforest','LMT','svmRadial','gbm'))
{
levels(data[,"y"]) = levels(dataT[,"y"]) = list(YES = positiveLabel, NO = negative_label)
positiveLabel = 'YES'
negative_label = 'NO'
}
results = NULL
min_ci_auc = AUC = max_ci_auc = KAPPA = DifferenceAUC = Precision = Specificity = Sensitivity = NPV = PPV = TP = FP = TN = FN = Concord = as.numeric(c())
ROCVAL = NULL
ROCVAL = data.frame(as.numeric(as.character(as.factor(ifelse(dataT[,"y"]== positiveLabel,1,0)))),stringsAsFactors=FALSE)
colnames(ROCVAL) = "D"
resPerf = data.frame( Parms = grpeParams$Parms)
resultsAUC = data.frame( Parms = grpeParams$Parms)
if (length(grpeNewCat)>0) {
df.Impt <- data.frame(structure(
rep(0,length(grpeNewCat)*(ncol(dataT)-1)), .Dim = c(length(grpeNewCat),ncol(dataT)-1),
.Dimnames = list(names(grpeNewCat),
colnames(dataT[ ,!(colnames(dataT) == "y")]))
))
}
else df.Impt = NULL
for (i in grpeParams$Parms) {
if(i %in% names(data))
{
dataTrain.ML = cbind(data["y"],data[i])
dataTest.ML = cbind(dataT["y"],dataT[i])
}
else
{
dataTrain.ML = cbind(data["y"],data[grpeNewCat[[i]]])
dataTest.ML = cbind(dataT["y"],dataT[grpeNewCat[[i]]])
}
if(i_model == "NoMod"){
dataTrain.ML.v <- ifelse(dataTrain.ML[,"y"] == positiveLabel, 1, 0)
print(dataTrain.ML.v)
dataTest.ML.v <- ifelse(dataTest.ML[,"y"] == positiveLabel, 1, 0)
print(dataTest.ML.v )
dataTrain.ML$y <- factor(dataTrain.ML.v)
dataTest.ML$y <- factor(dataTest.ML.v)
res.glm <- glm(y ~.,
family = binomial(),
data = dataTrain.ML)
#prediction
glm.pred <- predict.glm(res.glm, type = "response")
# Create struc of variable importants
if( i %in% rownames(df.Impt)){
varImpor = varImp (res.glm, scale = FALSE)
df.Impt[i,rownames(varImpor)] = round(varImpor$Overall,2)
}
ROCVAL = cbind(ROCVAL,data.frame(glm.pred))
colnames(ROCVAL) = c(colnames(ROCVAL)[-ncol(ROCVAL)],i)
Youden = as.numeric(c())
lab.predt <- function(t) as.vector(ifelse(glm.pred > t , 1,0))
thres = seq(.1, .9, by = 0.01)
for (threshold in thres){
cm<-caret::confusionMatrix(as.factor(lab.predt(threshold)),as.factor(dataTest.ML.v),positive = "1")
Sp = cm$byClass['Specificity']
Ss = cm$byClass['Sensitivity']
y = Sp+Ss-1
Youden = append(Youden,y)
}
a = caret::confusionMatrix(as.factor(lab.predt(thres[which.max(Youden)])), as.factor(dataTest.ML.v),positive = "1")
print(a)
rpartROC = roc(y ~ predict.glm(res.glm, type = "response"), data = dataTrain.ML,levels = c(0, 1),direction = "<")
Concord = append(Concord,round(Concordance(as.numeric(as.character(ifelse(dataTrain.ML$y == "1",1,0))), glm.pred)$Concordance,3))
Specificity = append(Specificity,round(a$byClass['Specificity'],2))
Precision = append(Precision,round(a$overall['Accuracy'],2))
Sensitivity = append(Sensitivity,round(a$byClass['Sensitivity'],2))
PPV = append(PPV,round(a$byClass['Pos Pred Value'],2))
NPV = append(NPV,round(a$byClass['Neg Pred Value'],2))
TP = append(TP,round(a$table[2,2],0))
TN = append(TN,round(a$table[1,1],0))
FP = append(FP,round(a$table[2,1],0))
FN = append(FN,round(a$table[1,2],0))
AUC = append(AUC,round(ci.auc(rpartROC)[2],2))
KAPPA = append(KAPPA,round(a$overall["Kappa"],2))
min_ci_auc = append(min_ci_auc,round(ci.auc(rpartROC)[1],2))
max_ci_auc = append(max_ci_auc,round(ci.auc(rpartROC)[3],2))
}
else if(i_model %in% c('glm','nb','lda','knn','gbm')){
set.seed(1)
if (i_model == 'gbm') {
fit <- train(dataTrain.ML[,-1], as.factor(dataTrain.ML[,"y"]),
method='gbm',
trControl=trCtr,
metric = "ROC",
preProc = c("center", "scale"))
print(fit)
}
else fit <- train(y ~.,
data = dataTrain.ML,
method = i_model ,
trControl = trCtr)
if( i %in% rownames(df.Impt)){
if(i_model == 'gbm')
{
summa = summary(fit)
df.Impt[i,levels(summa$var)] = round(summa$rel.inf,2)
}
else if(i_model == 'glm') {
varImpor = varImp (fit, scale = FALSE)
df.Impt[i,rownames(varImpor$importance)] = round(varImpor$importance$Overall,2)
}
else{
varImpor = varImp (fit, scale = FALSE)
df.Impt[i,rownames(varImpor$importance)] = round(varImpor$importance[,positiveLabel],2)
}
}
Youden = as.numeric(c())
fitpred <- as.vector(predict(fit,dataTest.ML,type = "prob"))
fitpredt <- function(t) as.factor(ifelse(fitpred[,positiveLabel] > t , positiveLabel,negative_label))
ROCVAL = cbind(ROCVAL,data.frame(fitpred[,positiveLabel]))
colnames(ROCVAL) = c(colnames(ROCVAL)[-ncol(ROCVAL)],i)
print(ROCVAL)
thres = seq(.1, .9, by = 0.01)
for (threshold in thres){
cm<-caret::confusionMatrix(as.factor(fitpredt(threshold)),as.factor(dataTrain.ML[,"y"]),positive = positiveLabel)
Sp = cm$byClass['Specificity']
Ss = cm$byClass['Sensitivity']
youd = Sp+Ss-1
Youden = append(Youden,youd)
}
Concord = append(Concord,round(Concordance(as.numeric(as.character(ifelse(dataTrain.ML[,"y"] == positiveLabel,1,0))), fitpred)$Concordance,2))
a = caret::confusionMatrix(as.factor(fitpredt(thres[which.max(Youden)])), as.factor(dataTrain.ML[,"y"]),positive = positiveLabel)
print(a)
rpartROC <- roc(y ~ predict(fit, type = "prob")[,positiveLabel], data = dataTest.ML,levels = c(positiveLabel, negative_label),direction = ">")
Specificity = append(Specificity,round(a$byClass['Specificity'],2))
Precision = append(Precision,round(a$overall['Accuracy'],2))
Sensitivity = append(Sensitivity,round(a$byClass['Sensitivity'],2))
PPV = append(PPV,round(a$byClass['Pos Pred Value'],2))
NPV = append(NPV,round(a$byClass['Neg Pred Value'],2))
TP = append(TP,round(a$table[1,1],0))
TN = append(TN,round(a$table[2,2],0))
FP = append(FP,round(a$table[1,2],0))
FN = append(FN,round(a$table[2,1],0))
AUC = append(AUC,round(ci.auc(rpartROC)[2],2))
KAPPA = append(KAPPA,round(a$overall["Kappa"],2))
min_ci_auc = append(min_ci_auc,round(ci.auc(rpartROC)[1],2))
max_ci_auc = append(max_ci_auc,round(ci.auc(rpartROC)[3],2))
}
else if(i_model == 'cforest'){
ctrl <- trainControl(method = "cv", number = 10, repeats = 3,
classProbs = TRUE, summaryFunction = twoClassSummary,
allowParallel = TRUE)
set.seed(1)
train.cf <- train(y ~ .,
data=dataTrain.ML,
method="cforest",
trControl=ctrl)
cf.probs = predict(train.cf,dataTrain.ML,type="prob")
ROCVAL = cbind(ROCVAL,data.frame(cf.probs[,positiveLabel]))
colnames(ROCVAL) = c(colnames(ROCVAL)[-ncol(ROCVAL)],i)
if( i %in% rownames(df.Impt)){
varImpor = varImp (train.cf, scale = FALSE)
df.Impt[i,rownames(varImpor$importance)] = varImpor$importance$Overall
}
fitpredt <- function(t) as.factor(ifelse(cf.probs[,positiveLabel] > t , positiveLabel,negative_label))
Youden = as.numeric(c())
thres = seq(.1, .9, by = 0.01)
for (threshold in thres){
#
cm<-caret::confusionMatrix(as.factor(fitpredt(threshold)),as.factor(dataTest.ML[,"y"]),positive = positiveLabel)
Sp = cm$byClass['Specificity']
Ss = cm$byClass['Sensitivity']
y = Sp+Ss-1
Youden = append(Youden,y)
}
a = caret::confusionMatrix(as.factor(fitpredt(thres[which.max(Youden)])), as.factor(dataTest.ML[,"y"]),positive = positiveLabel)
print(a)
rocCurve.cf <- roc(dataTest.ML[,"y"],cf.probs[,positiveLabel],levels = c(negative_label,positiveLabel),direction = "<")
Concord = append(Concord,round(Concordance(as.numeric(as.character(ifelse(dataTest.ML[,"y"] == positiveLabel,1,0))), cf.probs)$Concordance,2))
AUC = append(AUC,round(ci.auc(rocCurve.cf)[2],2))
Specificity = append(Specificity,round(a$byClass['Specificity'],2))
Precision = append(Precision,round(a$overall['Accuracy'],2))
Sensitivity = append(Sensitivity,round(a$byClass['Sensitivity'],2))
PPV = append(PPV,round(a$byClass['Pos Pred Value'],2))
NPV = append(NPV,round(a$byClass['Neg Pred Value'],2))
TP = append(TP,round(a$table[1,1],0))
TN = append(TN,round(a$table[2,2],0))
FP = append(FP,round(a$table[1,2],0))
FN = append(FN,round(a$table[2,1],0))
KAPPA = append(KAPPA,round(a$overall["Kappa"],2))
min_ci_auc = append(min_ci_auc,round(ci.auc(rocCurve.cf)[1],2))
max_ci_auc = append(max_ci_auc,round(ci.auc(rocCurve.cf)[3],2))
}
else if(i_model == "svmRadial"){
ctrl <- trainControl(method = "cv", number = 10, repeats = 3,
classProbs = TRUE, summaryFunction = twoClassSummary,
allowParallel = TRUE)
set.seed(2)
svmGrid = expand.grid(
.sigma = as.numeric(sigest(y ~.,data = dataTrain.ML,scaled=FALSE)),
.C = c(0.1, 0.25,0.5, 1,10)
)
svmTuned = train(
y ~ .,
data = dataTrain.ML,
method = "svmRadial",
tuneGrid = svmGrid,
metric = "ROC",
trControl = ctrl,
preProcess = NULL,
scaled = FALSE,
fit = FALSE)
svm_model = ksvm(y ~ .,
data = dataTrain.ML,
kernel = "rbfdot",
kpar = list(sigma=svmTuned$bestTune$sigma),
C = svmTuned$bestTune$C,
prob.model = TRUE,
scaled = FALSE)
predict_loan_status_svm = predict(svm_model,dataTrain.ML,type="decision")
if( i %in% rownames(df.Impt)){
M <- rminer::fit(y~., data=dataTrain.ML, model="svm", kpar=list(sigma=svmTuned$bestTune$sigma), C=svmTuned$bestTune$C)
df.Impt[i,colnames(dataTrain.ML[,-1])] = Importance(M, data=dataTrain.ML)$imp
}
rocCurve_svm = roc(response = as.factor(dataTrain.ML[,"y"]),
predictor = predict_loan_status_svm)
auc_curve = auc(rocCurve_svm)
ROCVAL = cbind(ROCVAL,predict_loan_status_svm)
colnames(ROCVAL) = c(colnames(ROCVAL)[-ncol(ROCVAL)],i)
cm1 = caret::confusionMatrix(as.factor(ifelse(predict_loan_status_svm < median(predict_loan_status_svm), positiveLabel,negative_label)),dataTrain.ML[,"y"])
cm2 = caret::confusionMatrix(as.factor(ifelse(predict_loan_status_svm < mean(predict_loan_status_svm), positiveLabel,negative_label)),dataTrain.ML[,"y"])
if (cm1$overall["Kappa"]>cm2$overall["Kappa"]) a = cm1 else a = cm2
print(a)
Concord = append(Concord,round(1-Concordance(as.numeric(as.character(ifelse(dataTrain.ML[,"y"] == positiveLabel,1,0))), predict_loan_status_svm)$Concordance,2))
KAPPA = append(KAPPA,round(a$overall['Kappa'],2))
AUC = append(AUC,round(auc_curve,2))
Specificity = append(Specificity,round(a$byClass['Specificity'],2))
Precision = append(Precision,round(a$overall['Accuracy'],2))
Sensitivity = append(Sensitivity,round(a$byClass['Sensitivity'],2))
PPV = append(PPV,round(a$byClass['Pos Pred Value'],2))
NPV = append(NPV,round(a$byClass['Neg Pred Value'],2))
TP = append(TP,a$table[1,1])
TN = append(TN,a$table[2,2])
FP = append(FP,a$table[1,2])
FN = append(FN,a$table[2,1])
min_ci_auc = append(min_ci_auc,round(ci.auc(rocCurve_svm)[1],2))
max_ci_auc = append(max_ci_auc,round(ci.auc(rocCurve_svm)[3],2))
}
else{
set.seed(1)
fit <- train(y ~.,
data = dataTrain.ML,
method = i_model ,
trControl = trainControl(method = "cv",
number = ,
savePredictions = TRUE,allowParallel = FALSE))
Youden = as.numeric(c())
fitpred <- as.vector(predict(fit,dataTest.ML,type = "prob"))
fitpredt <- function(t) as.factor(ifelse(fitpred[,positiveLabel] > t , positiveLabel,negative_label))
ROCVAL = cbind(ROCVAL,data.frame(fitpred[,positiveLabel]))
colnames(ROCVAL) = c(colnames(ROCVAL)[-ncol(ROCVAL)],i)
thres = seq(.1, .9, by = 0.01)
for (threshold in thres){
cm<-caret::confusionMatrix(as.factor(fitpredt(threshold)),as.factor(dataTest.ML[,"y"]),positive = positiveLabel)
Sp = cm$byClass['Specificity']
Ss = cm$byClass['Sensitivity']
youd = Sp+Ss-1
Youden = append(Youden,youd)
}
if( i %in% rownames(df.Impt)){
varImpor = varImp (fit, scale = FALSE)
df.Impt[i,rownames(varImpor$importance)] = round(varImpor$importance[,positiveLabel],2)
}
Concord = append(Concord,round(Concordance(as.numeric(as.character(ifelse(dataTrain.ML[,"y"] == positiveLabel,1,0))), fitpred)$Concordance,2))
a = caret::confusionMatrix(as.factor(fitpredt(thres[which.max(Youden)])), as.factor(dataTest.ML[,"y"]),positive = positiveLabel)
rpartROC <- roc(y ~ predict(fit, type = "prob")[,positiveLabel], data = dataTest.ML,levels = c(positiveLabel, negative_label),direction = ">")
print(a)
Specificity = append(Specificity,round(a$byClass['Specificity'],2))
Precision = append(Precision,round(a$overall['Accuracy'],2))
Sensitivity = append(Sensitivity,round(a$byClass['Sensitivity'],2))
PPV = append(PPV,round(a$byClass['Pos Pred Value'],2))
NPV = append(NPV,round(a$byClass['Neg Pred Value'],2))
TP = append(TP,round(a$table[2,2],0))
TN = append(TN,round(a$table[1,1],0))
FP = append(FP,round(a$table[2,1],0))
FN = append(FN,round(a$table[1,2],0))
AUC = append(AUC,round(ci.auc(rpartROC)[2],2))
KAPPA = append(KAPPA,round(a$overall["Kappa"],2))
min_ci_auc = append(min_ci_auc,round(ci.auc(rpartROC)[1],2))
max_ci_auc = append(max_ci_auc,round(ci.auc(rpartROC)[3],2))
}
}
resPerf = data.frame(
resPerf,
AUC,
Precision,
Specificity,
Sensitivity,
PPV,
NPV,
TP,
FP,
TN,
FN,
KAPPA,
Concord
)
resultsAUC = data.frame(
resultsAUC,
min_ci_auc,
AUC,
max_ci_auc
)
if(length(df.Impt)>0){ newdf = NULL
for (i in 1:nrow(df.Impt)) {
dfVarI_ = NULL
s = t(df.Impt[i,grpeNewCat[[i]]])
rownt = rownames(s)
dfVarI = data.frame(s,data.frame(rownt))
colnames(dfVarI) = c('Contribution','Parameters')
dfVarI_ = cbind(dfVarI,Categories = rep(rownames(df.Impt)[i],nrow(dfVarI)))
if (i == 1)
newdf = dfVarI_
else
newdf <- rbind(newdf, dfVarI_)
}
newdf2 <- newdf %>%
arrange(Categories, Contribution) %>%
mutate(Parameters = factor(Parameters, levels = unique(Parameters)))
if(ncol(ROCVAL)>2){
ROCVAL <- melt_roc(ROCVAL, "D", colnames(ROCVAL)[-1])
}else{
colnames(ROCVAL) = c("D","M1")
}
}else newdf2 = NULL
return(list(res = resPerf,ROCVALs = ROCVAL, resultsAUCPlot = resultsAUC,df.Impts = newdf2))
}