unsupervised randomForest should be supported by all functions except plot_predict_interaction

Author: Yue-Jiang

R/explain_forest.R

@@ -25,7 +25,7 @@ explain_forest <- function(forest, interactions = FALSE, data = NULL, vars = NUL
                            measures = NULL){
   if(is.null(measures)){
     if("randomForest" %in% class(forest)){
-      if(forest$type == "classification"){
+      if(forest$type %in% c("classification", "unsupervised")){
         measures <- c("mean_min_depth", "accuracy_decrease", "gini_decrease", "no_of_nodes", "times_a_root")
       } else{
         measures <- c("mean_min_depth", "mse_increase", "node_purity_increase", "no_of_nodes", "times_a_root")
@@ -36,7 +36,7 @@ explain_forest <- function(forest, interactions = FALSE, data = NULL, vars = NUL
   }
   if("randomForest" %in% class(forest) && dim(forest$importance)[2] == 1){
     stop(paste("Your forest does not contain information on local importance so",
-               ifelse(forest$type == "classification", "accuracy_decrease", "mse_increase"),
+               ifelse(forest$type %in% c("classification", "unsupervised"), "accuracy_decrease", "mse_increase"),
                "measure cannot be extracted.",
                "To add it regrow the forest with the option localImp = TRUE and run this function again."))
   }

R/measure_importance.R

@@ -31,7 +31,7 @@ measure_no_of_nodes_ranger <- function(forest_table){
 # Extract randomForest variable importance measures
 # randomForest
 measure_vimp <- function(forest, only_nonlocal = FALSE){
-  if(forest$type == "classification"){
+  if(forest$type %in% c("classification", "unsupervised")){
     if(dim(forest$importance)[2] == 1){
       if(only_nonlocal == FALSE){
         print("Warning: your forest does not contain information on local importance so 'accuracy_decrease' measure cannot be extracted. To add it regrow the forest with the option localImp = TRUE and run this function again.")
@@ -129,7 +129,7 @@ measure_importance <- function(forest, mean_sample = "top_trees", measures = NUL
 measure_importance.randomForest <- function(forest, mean_sample = "top_trees", measures = NULL){
   tree <- NULL; `split var` <- NULL; depth <- NULL
   if(is.null(measures)){
-    if(forest$type == "classification"){
+    if(forest$type %in% c("classification", "unsupervised")){
       measures <- c("mean_min_depth", "no_of_nodes", "accuracy_decrease",
                     "gini_decrease", "no_of_trees", "times_a_root", "p_value")
     } else if(forest$type =="regression"){
@@ -159,7 +159,7 @@ measure_importance.randomForest <- function(forest, mean_sample = "top_trees", m
     importance_frame <- merge(importance_frame, measure_no_of_nodes(forest_table), all = TRUE)
     importance_frame[is.na(importance_frame$no_of_nodes), "no_of_nodes"] <- 0
   }
-  if(forest$type == "classification"){
+  if(forest$type %in% c("classification", "unsupervised")){
     vimp <- c("accuracy_decrease", "gini_decrease")
   } else if(forest$type =="regression"){
     vimp <- c("mse_increase", "node_purity_increase")

R/min_depth_interactions.R

@@ -303,6 +303,10 @@ plot_predict_interaction.randomForest <- function(forest, data, variable1, varia
                                                   main = paste0("Prediction of the forest for different values of ",
                                                                 paste0(variable1, paste0(" and ", variable2))),
                                                   time = NULL){
+  if (forest$type == "unsupervised") {
+    warning("plot_predict_interaction cannot be performed on unsupervised random forests.")
+    return(NULL)
+  }
   newdata <- expand.grid(seq(min(data[[variable1]]), max(data[[variable1]]), length.out = grid),
                          seq(min(data[[variable2]]), max(data[[variable2]]), length.out = grid))
   colnames(newdata) <- c(variable1, variable2)