setwd(".")

options(stringsAsFactors = FALSE)

# library("clusterSim")

library("PRROC")

library("e1071")

source("./confusion_matrix_rates.r")

threshold <- 0.5

dataFileName <- "../data/LungCancerDataset_AllRecords_NORM_27reduced_features.csv"

cat("dataFileName = ", dataFileName, "\n", sep="")

cancer_data_norm <- read.csv(file=dataFileName,head=TRUE,sep=",",stringsAsFactors=FALSE)

cancer_data_norm <- cancer_data_norm[sample(nrow(cancer_data_norm)),] # shuffle the rows

totalElements <- dim(cancer_data_norm)[1]

subsets_size <- 4000

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)

target_index <- dim(cancer_data_norm)[2]

training_set_perce <- 60

cat("training_set_perce = ", training_set_perce, "% \n", sep="")

validation_set_perce <- 20

cat("validation_set_perce = ", validation_set_perce, "% \n", sep="")

test_set_perce <- 100 - training_set_perce - validation_set_perce

cat("test_set_perce = ", test_set_perce, "% \n", sep="")

# 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_perce/100) # NEW

# the validation set is the following 20% of the whole dataset

validation_set_first_index <- round(dim(cancer_data_norm)[1]*training_set_perce/100)+1 # NEW

validation_set_last_index <- round(dim(cancer_data_norm)[1]*(training_set_perce+validation_set_perce)/100) # NEW

# the test set is the last 20% of the whole dataset

test_set_first_index <- round(dim(cancer_data_norm)[1]*(training_set_perce+validation_set_perce)/100)+1 # NEW

test_set_last_index <- dim(cancer_data_norm)[1] # NEW

cat("[Creating the subsets for the values]\n")

cancer_data_train <- cancer_data_norm[training_set_first_index:training_set_last_index, 1:(target_index-1)] # NEW

cancer_data_validation <- cancer_data_norm[validation_set_first_index:validation_set_last_index, 1:(target_index-1)] # NEW

cancer_data_test <- cancer_data_norm[test_set_first_index:test_set_last_index, 1:(target_index-1)] # NEW

cat("[Creating the subsets 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_validation_labels <- cancer_data_norm[validation_set_first_index:validation_set_last_index, target_index] # NEW

cancer_data_test_labels <- cancer_data_norm[test_set_first_index:test_set_last_index, target_index]   # NEW

library(class)

library(gmodels)

# # The k value must be lower than the size of the trainingset

maxK <- 100 #NEW

mcc_array <- character(length(maxK))

# NEW PART:

cat("\n[Optimization of the hyper-parameter k start]\n")

# optimizaion loop

for(thisK in 1:maxK)

{

  # apply k-NN with the current K value

  # train on the training set, evaluate in the validation set by computing the MCC

  # save the MCC corresponding to the current K value

  cat("\n[Training the kNN model (with k=",thisK,") on training set & applying the kNN model to validation set]\n", sep="")

  cancer_data_validation_pred <- knn(train = cancer_data_train, test = cancer_data_validation, cl = cancer_data_train_labels, k=thisK)

  cancer_data_validation_pred_binary <- as.numeric (cancer_data_validation_pred)-1

  mcc_outcome <- mcc(cancer_data_validation_labels, cancer_data_validation_pred_binary)

  cat("When k=",thisK,", the MCC value is ",mcc_outcome, "\t (worst possible: -1; best possible: +1)\n", sep="")

  mcc_array[thisK] <- mcc_outcome

}

# select the k corresponding to the highest MCC and call it k_best

bestMCC <- max(mcc_array)

bestK <- match(bestMCC, mcc_array)

cat("\nThe best k value is ", bestK,", corresponding to MCC=", mcc_array[bestK],"\n", sep="")

cat("[Optimization end]\n\n")

cat("\n @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ \n")

# apply k-NN with k_best to the test set

cat("[Training the kNN model (with the OPTIMIZED hyper-parameter k=",bestK,") on training set & applying the kNN to the test set]\n", sep="")

cancer_data_test_pred <- knn(train = cancer_data_train, test = cancer_data_test, cl = cancer_data_train_labels, k=bestK)

cancer_data_test_pred <- as.numeric(cancer_data_test_pred)-1

confusion_matrix_rates(cancer_data_test_labels, cancer_data_test_pred, "@@@ Test set @@@")