main.R

main.R

@@ -4,14 +4,11 @@
 library(lightgbm)
 library(MLmetrics)
 
-# 1. Load your data
 df <- read.csv("./data/Ketamine_icp.csv")
 
-# --- 2. Data Preparation ---
 target_name <- "label"
 target_index <- which(names(df) == target_name)
 
-# Prepare target variable
 if (is.factor(df[, target_index])) {
   y <- as.numeric(df[, target_index]) - 1
 } else {
@@ -21,7 +18,6 @@ if (is.factor(df[, target_index])) {
 # Create the data matrix for features
 X <- as.matrix(df[, -target_index])
 
-# --- 3. Split Data into Training and Testing Sets ---
 set.seed(42)
 train_index <- sample(nrow(X), size = 0.8 * nrow(X))
 
@@ -30,7 +26,6 @@ X_test <- X[-train_index, ]
 y_train <- y[train_index]
 y_test <- y[-train_index]
 
-# --- 4. Get Feature Importance from Full Model ---
 lgb_train_full <- lgb.Dataset(data = X_train, label = y_train)
 
 params <- list(
@@ -64,9 +59,7 @@ results_df <- data.frame(
   Recall_class1 = numeric()
 )
 
-# --- 5. Loop through different numbers of top features ---
 cat("Training models with different numbers of top features...\n")
-cat("=====================================================\n")
 
 for (i in 1:num_features) {
   cat(paste("Training model with top", i, "features...\n"))
@@ -140,16 +133,12 @@ for (i in 1:num_features) {
             "| Recall:", round(recall, 4), "\n"))
 }
 
-cat("=====================================================\n")
 
-# --- 6. Display Results ---
-cat("\nSummary of Results:\n")
-cat("===================\n")
+cat("Summary of Results:\n")
 print(results_df)
 
 # Find best performing models based on different metrics
 cat("\nBest Performing Models:\n")
-cat("=======================\n")
 
 # Best by F1 score
 if (!all(is.na(results_df$F1_class1))) {
@@ -170,7 +159,6 @@ best_acc_idx <- which.max(results_df$Accuracy)
 cat(paste("Best Accuracy (", results_df$Accuracy[best_acc_idx],
           ") with", results_df$Num_Features[best_acc_idx], "features\n"))
 
-# --- 7. Optional: Plot metrics vs number of features ---
 if (require(ggplot2)) {
   library(ggplot2)
 
@@ -213,10 +201,7 @@ if (require(ggplot2)) {
   print(p3)
 }
 
-# --- 8. Save results to CSV ---
 write.csv(results_df, "feature_selection_results.csv", row.names = FALSE)
 cat("\nResults saved to 'feature_selection_results.csv'\n")
 
-# --- 9. Display top 20 feature importance plot ---
 lgb.plot.importance(importance, top_n = min(20, num_features))
-