model_with_caret.R

library(caret)
print("LOG: BEGIN")
raw_data <- read.csv("features.csv", header=TRUE, sep=",")

inTraining <- createDataPartition(raw_data$label, p = .75, list = FALSE)
training_data <- raw_data[ inTraining,]
testing_data  <- raw_data[-inTraining,]

train_control <- trainControl(method="repeatedcv", number=10, repeats=10)
# grid <- expand.grid(nIter=10, method='Adaboost.M1')
set.seed(825)
model <- train(label~tool_name+severity+redundancy_level+neighbors+category+clang_analyzer+frama_c+cppcheck+warnings_in_this_file+language, data=training_data, trControl=train_control, method="adaboost") #, tuneGrid=grid)
print("LOG: Model")
model


modelImp <- varImp(model, scale = FALSE)
modelImp
ggplot(modelImp)

predictions = predict(model, newdata = testing_data)
probabilities = predict(model, newdata = testing_data, type="prob")

confusion_matrix = confusionMatrix(predictions, testing_data$label, positive="true", mode="prec_recall")

print("LOG: Predictions")
predictions
print("LOG: Labels")
testing_data$label
print("LOG: Probabilities")
probabilities
print("LOG: Confusion Matrix")
confusion_matrix

print("LOG: Plotting...")
ggplot(model)
print("LOG: Saving...")
save.image()
print("LOG: END, model saved at .RData")

# properly plot importance
# barplot(iris.adaboost$imp[order(iris.adaboost$imp, decreasing = TRUE)], ylim=c(0, 100), main="Variables Relative Importance", col="lightblue")

# we may want to show the error evolution
# FOR THIS WE NEED TO TRAIN THE MODEL AGAIN FROM
#evol.test <- errorevol(iris.adaboost, iris[-train, ])
#evol.train <- errorevol(iris.adaboost, iris[train, ])
#plot(evol.test$error, type = "l", ylim = c(0, 1), main="Boosting error versus number of trees", xlab = "Iterations", ylab="Error", col="red", lwd=2)
#lines(evol.train$error, cex = .5, col = "blue", lty = 2, lwd=2)
#legend("topleft", c("test", "train"), col = c("red", "blue"), lty=1:2, lwd=2)