--- a
+++ b/bin/random_forest_class.r
@@ -0,0 +1,127 @@
+setwd(".")
+options(stringsAsFactors = FALSE)
+
+list.of.packages <- c("PRROC", "e1071", "randomForest","class", "gmodels", "formula.tools")
+new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
+if(length(new.packages)) install.packages(new.packages)
+
+library("PRROC")
+library("e1071")
+library("randomForest")
+library("class")
+library("gmodels")
+library("formula.tools")
+
+source("./confusion_matrix_rates.r")
+source("./utils.r")
+
+# args = commandArgs(trailingOnly=TRUE)
+# thisNtree <- as.integer(args[1])
+
+# thisNtree <- 5000
+
+threshold <- 0.5
+fileName <- "../data/LungCancerDataset_AllRecords_NORM_27reduced_features.csv"
+cancer_data_norm <- read.csv(file=fileName,head=TRUE,sep=",",stringsAsFactors=FALSE)
+cat("fileName = ", fileName, "\n", sep="")
+
+cat("[Randomizing the rows]\n")
+cancer_data_norm <- cancer_data_norm[sample(nrow(cancer_data_norm)),] # shuffle the rows
+
+totalElements <- dim(cancer_data_norm)[1]
+
+subsets_size <- 10443
+
+target_index <- dim(cancer_data_norm)[2]
+
+target_label <- colnames(cancer_data_norm[target_index])
+
+if (subsets_size != totalElements) {
+    cat("ATTENTION: We are running the method on a subset of the original dataset, \n", sep="")
+    cat(" containing only ", subsets_size, " elements \n", sep="")
+    cat(" instead of ", totalElements, " elements \n", sep="")
+}
+
+cancer_data_norm <- cancer_data_norm[1:subsets_size, ]
+
+dataset_dim_retriever(cancer_data_norm)
+imbalance_retriever(cancer_data_norm$Metastasis)
+
+training_set_perc=50
+INPUT_PERC_POS <- 50
+cat("[training set = ", training_set_perc,"%]\n", sep="")
+cat("[test set = ", (100-training_set_perc),"%]\n", sep="")
+
+artificialBalance <- TRUE
+balancedFlag <- TRUE # flag that sets everything to 50% 50% ratio
+
+if (artificialBalance == TRUE) {
+
+
+    train_data_balancer_output <- train_data_balancer(cancer_data_norm, target_index, training_set_perc, INPUT_PERC_POS, balancedFlag)
+
+    cancer_data_train <- train_data_balancer_output[[1]]
+    cancer_data_test <- train_data_balancer_output[[2]]
+    
+     # Creating the subsets for the targets
+    cancer_data_train_labels <- cancer_data_train[, target_index] # NEW
+     cancer_data_test_labels <- cancer_data_test[, target_index]   # NEW
+
+} else {
+
+
+   # the training set is the first 60% of the whole dataset
+    training_set_first_index <- 1 # NEW
+    training_set_last_index <- round(dim(cancer_data_norm)[1]*training_set_perc/100) # NEW
+
+    # the test set is the last 40% of the whole dataset
+    test_set_first_index <- training_set_last_index+1 # NEW
+    test_set_last_index <- dim(cancer_data_norm)[1] # NEW
+
+    cat("[Creating the training set and test set for the values]\n")
+    cancer_data_train <- cancer_data_norm[training_set_first_index:training_set_last_index, 1:(target_index)] # NEW
+    cancer_data_test <- cancer_data_norm[test_set_first_index:test_set_last_index, 1:(target_index)] # NEW
+    
+    cat("[training set dimensions: ", dim(cancer_data_train)[1], " patients]\n")
+
+    cat("[test set dimensions: ", dim(cancer_data_test)[1], " patients]\n")
+
+    cat("[Creating the training set and test set for the labels \"1\"-\"0\"]\n")
+    cancer_data_train_labels <- cancer_data_norm[training_set_first_index:training_set_last_index, target_index] # NEW
+    cancer_data_test_labels <- cancer_data_norm[test_set_first_index:test_set_last_index, target_index]   # NEW
+
+}
+
+
+dataset_dim_retriever(cancer_data_train)
+imbalance_retriever(cancer_data_train$Metastasis)
+
+
+cat("\n[Training the random forest classifier on the training set]\n")
+
+# rf_new <- randomForest(Metastasis ~ ., data=cancer_data_train, importance=TRUE, proximity=TRUE, ntree=thisNtree)
+allFeaturesFormula <- Metastasis ~ .
+thisFormulaTop2features <- Metastasis ~ DerivedSS1977 + RXSumm..SurgOthReg.Dis.2003..
+thisFormulaTop3features <- Metastasis ~ DerivedSS1977 + RXSumm..SurgOthReg.Dis.2003.. + T
+thisFormula_TN <- Metastasis ~ T + N
+thisFormula_TNTumorSize <- Metastasis ~ T + N + TumorSize
+
+thisFormula_TNTumorSizeAge <- Metastasis ~ T + N + TumorSize + Age # top predictions among the non-metastasis features
+
+thisFormula_TNAge <- Metastasis ~ T + N  + Age
+thisFormula_TAge <- Metastasis ~ T  + Age 
+
+selectedFormula <- thisFormula_TAge
+rf_new <- randomForest(selectedFormula, data=cancer_data_train, importance=TRUE, proximity=TRUE)
+cat("\nFeatures used in this prediction: \t", as.character(selectedFormula), "\n\n", sep="")
+
+
+cat("\n[Applying the trained random forest classifier on the test set]\n")
+cancer_data_test_PRED <- predict(rf_new, cancer_data_test, type="response")
+
+confusion_matrix_rates(cancer_data_test_labels, cancer_data_test_PRED, "@@@ Test set @@@")
+
+# mcc_outcome <- mcc(cancer_data_test_labels, cancer_data_test_PRED_binary)
+
+
+