added ranger support

DESCRIPTION

@@ -11,14 +11,9 @@ Authors@R: c(
            family = "Gnasso",
            role = "aut",
            email = "agostino.gnasso@unina.it",
-           comment = c(ORCID = "0000-0002-8046-3923")),
-           person(given = "Srikanth",
-           family = "Komala Sheshachala",
-           role = "ctb",
-           email = "sri.teach@gmail.com",
-           comment = c(ORCID = "0000-0002-1865-5668"))
+           comment = c(ORCID = "0000-0002-8046-3923"))
            )
-Description: The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria et al. (2024) <doi:10.1007/s00180-022-01312-6>. It aims to explain and interpret decision tree ensemble models using a single tree-like structure. 'e2tree' is a new way of explaining an ensemble tree trained through 'randomForest', 'xgboost' or 'ranger' packages.
+Description: The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria et al. (2024) <doi:10.1007/s00180-022-01312-6>. It aims to explain and interpret decision tree ensemble models using a single tree-like structure. 'e2tree' is a new way of explaining an ensemble tree trained through 'randomForest' or 'xgboost' packages.
 License: MIT + file LICENSE
 URL: https://github.com/massimoaria/e2tree
 BugReports: https://github.com/massimoaria/e2tree/issues
@@ -36,6 +31,7 @@ Imports:
     partitions,
     purrr,
     tidyr,
+    ranger,
     randomForest,
     rpart.plot,
     Rcpp,
@@ -44,6 +40,5 @@ Imports:
 LazyData: true
 LinkingTo: Rcpp
 Suggests: 
-    testthat (>= 3.0.0),
-    ranger (>= 0.6.0),
+    testthat (>= 3.0.0)
 Config/testthat/edition: 3

NAMESPACE

@@ -57,6 +57,7 @@ importFrom(foreach,"%dopar%")
 importFrom(foreach,foreach)
 importFrom(grDevices,colorRampPalette)
 importFrom(randomForest,randomForest)
+importFrom(ranger,ranger)
 importFrom(rpart.plot,rpart.plot)
 importFrom(tidyr,complete)
 importFrom(tidyr,drop_na)

R/createDisMatrix.R

@@ -44,8 +44,13 @@ utils::globalVariables(c("resp", "W", "data_XGB")) # to avoid CRAN check errors
 #' response_validation <- validation[,5]
 #'
 #' # Perform training:
-#' ensemble <- randomForest::randomForest(Species ~ ., data=training,
+#' ## "randomForest" package
+#' ensemble <- randomForest::randomForest(Species ~ ., data=training, 
 #' importance=TRUE, proximity=TRUE)
+#' 
+#' ## "ranger" package
+#' ensemble <- ranger::ranger(Species ~ ., data = iris, 
+#' num.trees = 1000, importance = 'impurity')
 #'
 #' D <- createDisMatrix(ensemble, data=training,
 #'                      label = "Species",
@@ -64,8 +69,13 @@ utils::globalVariables(c("resp", "W", "data_XGB")) # to avoid CRAN check errors
 #' response_validation <- validation[,1]
 #'
 #' # Perform training
-#' ensemble = randomForest::randomForest(mpg ~ ., data=training, ntree=1000,
+#' ## "randomForest" package
+#' ensemble = randomForest::randomForest(mpg ~ ., data=training, ntree=1000, 
 #' importance=TRUE, proximity=TRUE)
+#' 
+#' ## "ranger" package
+#' ensemble <- ranger::ranger(formula = mpg ~ ., data = training, 
+#' num.trees = 1000, importance = "permutation")
 #'
 #' D = createDisMatrix(ensemble, data=training,
 #'                         label = "mpg",

R/e2tree.R

@@ -38,9 +38,14 @@ utils::globalVariables(c("node", "Y", "p", "variable", "decImp", "splitLabel", "
 #' response_validation <- validation[,5]
 #'
 #' # Perform training:
+#' ## "randomForest" package
 #' ensemble <- randomForest::randomForest(Species ~ ., data=training, 
 #' importance=TRUE, proximity=TRUE)
 #' 
+#' ## "ranger" package
+#' ensemble <- ranger::ranger(Species ~ ., data = iris, 
+#' num.trees = 1000, importance = 'impurity')
+#' 
 #' D <- createDisMatrix(ensemble, data=training, label = "Species", 
 #'                               parallel = list(active=FALSE, no_cores = 1))
 #' 
@@ -61,9 +66,14 @@ utils::globalVariables(c("node", "Y", "p", "variable", "decImp", "splitLabel", "
 #' response_validation <- validation[,1]
 #' 
 #' # Perform training
+#' ## "randomForest" package
 #' ensemble = randomForest::randomForest(mpg ~ ., data=training, ntree=1000, 
 #' importance=TRUE, proximity=TRUE)
 #' 
+#' ## "ranger" package
+#' ensemble <- ranger::ranger(formula = mpg ~ ., data = training, 
+#' num.trees = 1000, importance = "permutation")
+#' 
 #' D = createDisMatrix(ensemble, data=training, label = "mpg", 
 #'                                parallel = list(active=FALSE, no_cores = 1))  
 #' 
@@ -95,13 +105,20 @@ e2tree <- function(formula, data, D, ensemble, setting=list(impTotal=0.1, maxDec
   }
 
   # Validate ensemble
-  if (!inherits(ensemble, "randomForest")) {
-    stop("Error: 'ensemble' must be a trained 'randomForest' model.")
-  }
-
-  # Validate ensemble type
-  if (!ensemble$type %in% c("classification", "regression")) {
-    stop("Error: 'type' in ensemble object must be either 'classification' or 'regression'.")
+  if (inherits(ensemble, "randomForest")) {
+    type <- ensemble$type
+    if (!type %in% c("classification", "regression")) {
+      stop("Error: 'type' in ensemble object must be 'classification' or 'regression'.")
+    }
+
+  } else if (inherits(ensemble, "ranger")) {
+    type <- ensemble$treetype
+    if (!type %in% c("Classification", "Regression")) {
+      stop("Error: 'type' in ensemble object must be 'classification' or 'regression'.")
+    }
+
+  } else {
+    stop("Error: 'ensemble' must be a trained 'randomForest' or 'ranger' model.")
   }
 
   # Validate setting
@@ -128,7 +145,16 @@ e2tree <- function(formula, data, D, ensemble, setting=list(impTotal=0.1, maxDec
 
   response <- mf[,1]
   X <- mf[,-1]
-  type <- ensemble$type
+
+  # create type object
+  if (inherits(ensemble, "randomForest")) {
+    type <- ensemble$type  # "classification" or "regression"
+
+  } else if (inherits(ensemble, "ranger")) {
+    # Convert "Classification" or "Regression" in lower case
+    type <- tolower(ensemble$treetype)
+  }
+
 
   setting$tMax <- 1
 
@@ -260,7 +286,7 @@ e2tree <- function(formula, data, D, ensemble, setting=list(impTotal=0.1, maxDec
         #info$impTotal[t] <- results$impTotal
         info$obs[t] <- list(index)
         info$path[t] <- paths(info,t)
-      } else if (suppressWarnings(Wtest(Y=response[index], X=S[index,s], p.value=0.05, type = ensemble$type))){
+      } else if (suppressWarnings(Wtest(Y=response[index], X=S[index,s], p.value=0.05, type = type))){
         # Stopping Rule with Mann-Whitney for regression case
         # if it is regression, check that the hypothesis that the two distributions in tL and tR are equal is rejected
 

R/eStoppingRules.R

@@ -1,9 +1,18 @@
 eStoppingRules <- function(y,index,t, setting, response, ensemble, vart1){
   n <- length(index)
-
+
+  # create type object
+  if (inherits(ensemble, "randomForest")) {
+    type <- ensemble$type  # "classification" o "regression"
+
+  } else if (inherits(ensemble, "ranger")) {
+    # Convert "Classification" or "Regression" in lower case
+    type <- tolower(ensemble$treetype)
+  }
+
   if (n>1){
     impTotal <- meanDis(y[index,index])
-    switch(ensemble$type,
+    switch(type,
            classification = {
              res <- as.numeric(moda(response[index])[2])
            },

R/rpart2Tree.R

@@ -32,9 +32,14 @@ utils::globalVariables(c("n","prob", "terminal"))
 #' response_validation <- validation[,5]
 #'
 #' # Perform training:
+#' ## "randomForest" package
 #' ensemble <- randomForest::randomForest(Species ~ ., data=training, 
 #' importance=TRUE, proximity=TRUE)
 #' 
+#' ## "ranger" package
+#' ensemble <- ranger::ranger(Species ~ ., data = iris, 
+#' num.trees = 1000, importance = 'impurity')
+#' 
 #' D <- createDisMatrix(ensemble, data=training, label = "Species", 
 #'                              parallel = list(active=FALSE, no_cores = 1))
 #' 
@@ -60,9 +65,14 @@ utils::globalVariables(c("n","prob", "terminal"))
 #' response_validation <- validation[,1]
 #' 
 #' # Perform training
+#' ## "randomForest" package
 #' ensemble = randomForest::randomForest(mpg ~ ., data=training, ntree=1000, 
 #' importance=TRUE, proximity=TRUE)
 #' 
+#' ## "ranger" package
+#' ensemble <- ranger::ranger(formula = mpg ~ ., data = training, 
+#' num.trees = 1000, importance = "permutation")
+#' 
 #' D = createDisMatrix(ensemble, data=training, label = "mpg", 
 #'                        parallel = list(active=FALSE, no_cores = 1))  
 #' 
@@ -88,24 +98,38 @@ rpart2Tree <- function(fit, ensemble){
     stop("Error: 'fit' must be an 'e2tree' object.")
   }
 
-  # Validate 'ensemble' (must be a trained 'randomForest' model)
-  if (!inherits(ensemble, "randomForest")) {
-    stop("Error: 'ensemble' must be a trained 'randomForest' model.")
+  # Validate 'ensemble' (must be a trained 'randomForest' or 'ranger' model)
+  if (inherits(ensemble, "randomForest")) {
+    type <- ensemble$type
+    if (!type %in% c("classification", "regression")) {
+      stop("Error: 'type' in ensemble object must be either 'classification' or 'regression'.")
+    }
+
+  } else if (inherits(ensemble, "ranger")) {
+    type <- ensemble$treetype
+    if (!type %in% c("Classification", "Regression")) {
+      stop("Error: 'type' in ensemble object must be either 'classification' or 'regression'.")
+    }
+
+  } else {
+    stop("Error: 'ensemble' must be a trained 'randomForest' or 'ranger' model.")
   }
 
   # Validate that 'fit$tree' exists and is a data frame
   if (!is.data.frame(fit$tree)) {
     stop("Error: 'fit$tree' must be a data frame.")
   }
-
-  # Validate that 'ensemble$type' is either 'classification' or 'regression'
-  if (!ensemble$type %in% c("classification", "regression")) {
-    stop("Error: 'type' in ensemble object must be either 'classification' or 'regression'.")
-  }
 
   # === Proceed with the function ===
 
-  type <- ensemble$type
+  # create type object
+  if (inherits(ensemble, "randomForest")) {
+    type <- ensemble$type  # "classification" or "regression"
+
+  } else if (inherits(ensemble, "ranger")) {
+    # Convert "Classification" or "Regression" in lower case
+    type <- tolower(ensemble$treetype)
+  }
 
   frame <- fit$tree
 

R/zzz.R

@@ -9,6 +9,7 @@ utils::globalVariables(".")
 #' @importFrom Rcpp sourceCpp evalCpp
 #' @import RSpectra
 #' @importFrom randomForest randomForest 
+#' @importFrom ranger ranger 
 #' @importFrom foreach foreach
 #' @importFrom foreach %dopar%
 #' @importFrom dplyr %>%

README.Rmd

@@ -13,7 +13,7 @@ output: github_document
 <!-- badges: end -->
 
 <p align="center">
-<img src="man/figures/e2tree_logo.png" width="400"  />
+<img src="e2tree_logo.png" width="400"  />
 </p>
 
 
@@ -45,12 +45,13 @@ remotes::install_github("massimoaria/e2tree")
 You can install the released version of e2tree from [CRAN](https://CRAN.R-project.org) with:
 
 ```{r eval=FALSE}
-install.packages("e2tree")
+if (!require("e2tree", quietly=TRUE)) install.packages("e2tree")
 ```
 
 ```{r warning=FALSE, message=FALSE}
 require(e2tree)
 require(randomForest)
+require(ranger)
 require(dplyr)
 require(ggplot2)
 if (!(require(rsample, quietly=TRUE))){install.packages("rsample"); require(rsample, quietly=TRUE)} 
@@ -106,8 +107,12 @@ response_validation <- validation[,5]
 Train an Random Forest model with 1000 weak learners
 
 ```{r}
-# Perform training:
-ensemble = randomForest(Species ~ ., data = training, importance = TRUE, proximity = TRUE)
+# Perform training with "ranger" or "randomForest" package:
+## RF with "ranger" package
+ensemble <- ranger(Species ~ ., data = training, num.trees = 1000, importance = 'impurity')
+
+## RF with "randomForest" package
+#ensemble = randomForest(Species ~ ., data = training, importance = TRUE, proximity = TRUE)
 ```
 
 Here, we create the dissimilarity matrix between observations through the createDisMatrix function
@@ -157,13 +162,21 @@ pred <- ePredTree(tree, training[,-5], target="virginica")
 Comparison of predictions (training sample) of RF and e2tree
 
 ```{r}
-table(pred$fit, ensemble$predicted)
+# "ranger" package
+table(pred$fit, ensemble$predictions)
+
+# "randomForest" package
+#table(pred$fit, ensemble$predicted)
 ```
 
 Comparison of predictions (training sample) of RF and correct response
 
 ```{r}
-table(ensemble$predicted, response_training)
+# "ranger" package
+table(ensemble$predictions, response_training)
+
+## "randomForest" package
+#table(ensemble$predicted, response_training)
 ```
 
 Comparison of predictions (training sample) of e2tree and correct response
@@ -175,11 +188,6 @@ table(pred$fit,response_training)
 Variable Importance
 
 ```{r}
-ensemble_imp <- ensemble$importance %>% as.data.frame %>% 
-  mutate(Variable = rownames(ensemble$importance),
-         RF_Var_Imp = round(MeanDecreaseAccuracy,2)) %>% 
-  select(Variable, RF_Var_Imp)
-
 V <- vimp(tree, training)
 V
 
@@ -189,26 +197,22 @@ V
 Comparison with the validation sample
 
 ```{r}
-ensemble.pred <- predict(ensemble, validation[,-5], proximity = TRUE)
+ensemble.pred <- predict(ensemble, validation[,-5])
 
 pred_val<- ePredTree(tree, validation[,-5], target="virginica")
 ```
 
 Comparison of predictions (sample validation) of RF and e2tree
 
 ```{r}
-table(pred_val$fit, ensemble.pred$predicted)
-```
-
-Comparison of predictions (validation sample) of RF and correct response
+## "ranger" package
+table(pred_val$fit, ensemble.pred$predictions)
 
-```{r}
-table(ensemble.pred$predicted, response_validation)
-ensemble.prob <- predict(ensemble, validation[,-5], proximity = TRUE, type="prob")
-roc_ensemble<- roc(response_validation, ensemble.prob$predicted[,"virginica"], target="virginica")
-roc_ensemble$auc
+## "randomForest" package
+#table(pred_val$fit, ensemble.pred$predicted)
 ```
 
+
 Comparison of predictions (validation sample) of e2tree and correct response
 
 ```{r}