build_final_onlyhx.R

#Fit final XGBoost model (only history) and evaluate on test set (Github)

library(readr)
library(dplyr)
library(reshape2)
library(parallel)
library(caret)
library(xgboost)
library(doMC)
library(pROC)
library(keras)
registerDoMC(5) #for parallelization


#1) create train/test split
load('./Results/5v_sparseMatrix_onlyhx.RData')
load('./Results/5v_indeces_list.RData')
indeces <- indeces_list[[1]]
x <- dataset$x
y <- dataset$y
rm(dataset)

x_test <- x[indeces$i_test,]
y_test <- y[indeces$i_test]

x_train <- x[-indeces$i_test,]
y_train <- y[-indeces$i_test]

rm(x); rm(y)

#2) build xgboost on all data except test set, using the tuned parameters

bst_onlyhx <- xgboost(data = x_train, label = y_train,
               max_depth = 20, eta = 0.3,
               nthread = 5, nrounds = 30,
               eval_metric = 'auc',
               objective = "binary:logistic",
               colsample_bylevel = 0.05)

bst_pred_test_onlyhx <- predict(bst_onlyhx, x_test)
roc(y_test, bst_pred_test_onlyhx)
ci.auc(roc(y_test, bst_pred_test_onlyhx), conf.level = 0.95)

save(bst_pred_test_onlyhx, file = './Results/5v_bst_pred_test_onlyhx.RData')