Correction BA + remove parallellogger

DESCRIPTION

@@ -16,7 +16,8 @@ Imports:
     farff,
     Rcpp (>= 1.0.5),
     RcppParallel,
-    stringr
+    stringr,
+    caret
 Encoding: UTF-8
 LinkingTo: Rcpp, BH (>= 1.51.0), RcppParallel
 RoxygenNote: 7.2.3

NAMESPACE

@@ -7,6 +7,7 @@ export(trainExplore)
 import(Rcpp)
 import(checkmate)
 importFrom(RcppParallel,RcppParallelLibs)
+importFrom(caret,confusionMatrix)
 importFrom(farff,writeARFF)
 importFrom(stringr,str_extract)
 importFrom(stringr,str_replace_all)

R/MainFunctions.R

@@ -51,11 +51,11 @@ trainExplore <- function(train_data = NULL,
                          Subsumption = TRUE,
                          BranchBound = TRUE,
                          Parallel = FALSE) {
-
+  
   if (!dir.exists(output_path)) {
     dir.create(output_path, recursive = TRUE)
-    }
-
+  }
+  
   # Create output folder
   if(!endsWith(output_path, "/")) {
     warning("Output path should end with /, add this")
@@ -363,10 +363,11 @@ modelsCurveExplore <- function(train_data = NULL,
                                      PrintSettings = PrintSettings, PrintPerformance = PrintPerformance,
                                      Subsumption = Subsumption, BranchBound = BranchBound,
                                      Parallel = Parallel)
+
       return(model)
     })
   },
-  finally = ParallelLogger::logInfo('No model for specificity.')
+  finally = warning("No model for specificity.")
   )
 
   return(modelsCurve)
@@ -377,14 +378,15 @@ modelsCurveExplore <- function(train_data = NULL,
 #'
 #' @return auc value for EXPLORE
 #' @export
+#' @importFrom caret confusionMatrix
 rocCurveExplore <- function(modelsCurve, data, labels) { # labels <- cohort$outcomeCount
 
   # TODO: input checks?
 
   # Combine all these results
   curve_TPR <- c(1,0)
   curve_FPR <- c(1,0)
-
+  
   for (c in length(modelsCurve):1) {
     model <- modelsCurve[c]
 
@@ -394,7 +396,7 @@ rocCurveExplore <- function(modelsCurve, data, labels) { # labels <- cohort$outc
     # Compute metrics
     conf_matrix <- table(factor(predict, levels = c(0,1)), factor(labels, levels = c(0,1))) # binary prediction
     performance <- caret::confusionMatrix(conf_matrix, positive = '1')
-
+    
     curve_TPR[c+2] <- performance$byClass['Sensitivity']
     curve_FPR[c+2] <- 1 - performance$byClass['Specificity']
   }

inst/examples/iris.project

@@ -24,6 +24,7 @@ FeatureRule=
 [Constraints]
 Maximize=ACCURACY
 Accuracy=
+BalancedAccuracy=
 Specificity=
 [Output]
 OutputMethod=BEST

inst/settings/template.project

@@ -24,6 +24,7 @@ FeatureRule=
 [Constraints]
 Maximize=ACCURACY
 Accuracy=
+BalancedAccuracy=
 Specificity=
 [Output]
 OutputMethod=BEST