Faster conditional sampling for multiple rows

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: arf
 Title: Adversarial Random Forests
-Version: 0.2.0
-Date: 2024-01-24
+Version: 0.2.2
+Date: 2024-05-24
 Authors@R: 
     c(person(given = "Marvin N.",
              family = "Wright",
@@ -41,10 +41,11 @@ Imports:
     data.table,
     ranger,
     foreach,
+    stringr,
     truncnorm
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.0
+RoxygenNote: 7.3.1
 Suggests: 
     ggplot2,
     doParallel,

NAMESPACE

@@ -7,10 +7,15 @@ export(forge)
 export(lik)
 import(data.table)
 import(ranger)
+import(stringr)
 importFrom(foreach,"%do%")
 importFrom(foreach,"%dopar%")
 importFrom(foreach,foreach)
+importFrom(foreach,getDoParWorkers)
+importFrom(stats,dunif)
 importFrom(stats,predict)
+importFrom(stats,punif)
+importFrom(stats,rbinom)
 importFrom(stats,runif)
 importFrom(truncnorm,dtruncnorm)
 importFrom(truncnorm,etruncnorm)

NEWS.md

@@ -1,3 +1,9 @@
+# arf 0.2.2
+* Faster and vectorized conditional sampling
+* Use min.bucket argument from ranger to avoid pruning if possible
+* Option to sample NAs in generated data if original data contains NAs
+* Stepsize in forge() to reduce memory usage
+* Option for local and global finite bounds
 
 # arf 0.2.0
 * Vectorized adversarial resampling

R/adversarial_rf.R

@@ -104,14 +104,18 @@ adversarial_rf <- function(
   dat <- rbind(data.frame(y = 1L, x_real),
                data.frame(y = 0L, x_synth))
   if (isTRUE(parallel)) {
-    rf0 <- ranger(y ~ ., dat, keep.inbag = TRUE, classification = TRUE, 
-                  num.trees = num_trees, min.node.size = 2L * min_node_size, 
-                  respect.unordered.factors = TRUE, ...)
+    num.threads <- NULL
   } else {
-    rf0 <- ranger(y ~ ., dat, keep.inbag = TRUE, classification = TRUE, 
-                  num.trees = num_trees, min.node.size = 2L * min_node_size, 
-                  respect.unordered.factors = TRUE, num.threads = 1L, ...)
+    num.threads <- 1L
   }
+  if (utils::packageVersion("ranger") >= "0.16.1") {
+    min.bucket <- c(min_node_size, 0)
+  } else {
+    min.bucket <- min_node_size
+  }
+  rf0 <- ranger(y ~ ., dat, keep.inbag = TRUE, classification = TRUE, 
+                num.trees = num_trees, min.bucket = min.bucket, 
+                respect.unordered.factors = TRUE, num.threads = num.threads, ...)
 
   # Recurse
   iters <- 0L
@@ -159,15 +163,9 @@ adversarial_rf <- function(
       dat <- rbind(data.frame(y = 1L, x_real),
                    data.frame(y = 0L, x_synth))
       # Train discriminator
-      if (isTRUE(parallel)) {
-        rf1 <- ranger(y ~ ., dat, keep.inbag = TRUE, classification = TRUE, 
-                      num.trees = num_trees, min.node.size = 2 * min_node_size, 
-                      respect.unordered.factors = TRUE, ...)
-      } else {
-        rf1 <- ranger(y ~ ., dat, keep.inbag = TRUE, classification = TRUE, 
-                      num.trees = num_trees, min.node.size = 2 * min_node_size, 
-                      respect.unordered.factors = TRUE, num.threads = 1, ...)
-      }
+      rf1 <- ranger(y ~ ., dat, keep.inbag = TRUE, classification = TRUE, 
+                    num.trees = num_trees, min.bucket = min.bucket, 
+                    respect.unordered.factors = TRUE, num.threads = num.threads, ...)
       # Evaluate
       acc0 <- 1 - rf1$prediction.error
       acc <- c(acc, acc0)
@@ -191,22 +189,29 @@ adversarial_rf <- function(
   if (isTRUE(prune)) {
     pred <- stats::predict(rf0, x_real, type = 'terminalNodes')$predictions + 1L
     prune <- function(tree) {
+      # Nodes to prune are leaves which contain fewer than min_node_size real samples
       out <- rf0$forest$child.nodeIDs[[tree]]
       leaves <- which(out[[1]] == 0L)
       to_prune <- leaves[!(leaves %in% which(tabulate(pred[, tree]) >= min_node_size))]
       while(length(to_prune) > 0) {
+        if (1 %in% to_prune) {
+          # Never prune the root
+          break
+        }
         for (tp in to_prune) {
-          # Find parents
+          # Find parent
           parent <- which((out[[1]] + 1L) == tp)
           if (length(parent) > 0) {
-            # Left child
+            # If node to prune (tp) is the left child of parent, replace left child with right child
             out[[1]][parent] <- out[[2]][parent]
           } else {
-            # Right child
+            # If node to prune (tp) is the right child of parent, replace right child with left child
             parent <- which((out[[2]] + 1L) == tp)
             out[[2]][parent] <- out[[1]][parent]
           }
         }
+        # If both children of a parent are to be pruned, prune the parent in the next round
+        # This happens if both children have been pruned
         to_prune <- which((out[[1]] + 1L) %in% to_prune)
       }
       return(out)

R/expct.R

@@ -34,7 +34,7 @@
 #' arf <- adversarial_rf(iris)
 #' psi <- forde(arf, iris)
 #' 
-#' # What is the expected value Sepal.Length?
+#' # What is the expected value of Sepal.Length?
 #' expct(psi, query = "Sepal.Length")
 #' 
 #' # What if we condition on Species = "setosa"?
@@ -61,21 +61,20 @@ expct <- function(
 
   # To avoid data.table check issues
   variable <- tree <- f_idx <- cvg <- wt <- V1 <- value <- val <- family <-
-    mu <- sigma <- obs <- prob <- . <- NULL
+    mu <- sigma <- obs <- prob <- f_idx_uncond <- . <- NULL
 
   # Prep evidence
   conj <- FALSE
-  if (!is.null(evidence)) {
-    evidence <- prep_evi(params, evidence)
-    if (!all(c('f_idx', 'wt') %in% colnames(evidence))) {
-      conj <- TRUE
-    }
-  } 
+  if (!is.null(evidence) && !(ncol(evidence) == 2 && all(c("f_idx", "wt") %in% colnames(evidence)))) {
+    evidence_variable <- prep_cond(evidence, params, "or")$variable
+    conj <- TRUE
+  }
+
 
   # Check query
   if (is.null(query)) {
     if (isTRUE(conj)) {
-      query <- setdiff(params$meta$variable, evidence$variable)
+      query <- setdiff(params$meta$variable, evidence_variable)
     } else {
       query <- params$meta$variable
       if (!is.null(evidence)) {
@@ -97,7 +96,7 @@ expct <- function(
     omega[, wt := cvg / num_trees]
     omega[, cvg := NULL]
   } else if (conj) {
-    omega <- leaf_posterior(params, evidence)
+    omega <- cforde(params, evidence, "or")$forest[, .(f_idx = f_idx_uncond, wt = cvg)]
   } else {
     omega <- evidence
   }