Allow vector min.node.size/min.bucket for class-wise limits

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: ranger
 Type: Package
 Title: A Fast Implementation of Random Forests
-Version: 0.16.0
-Date: 2023-11-09
+Version: 0.16.1
+Date: 2024-05-15
 Author: Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb]
 Maintainer: Marvin N. Wright <[email protected]>
 Description: A fast implementation of Random Forests, particularly suited for high
@@ -19,7 +19,7 @@ Suggests:
     survival,
     testthat
 Encoding: UTF-8
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 URL: http://imbs-hl.github.io/ranger/,
     https://github.com/imbs-hl/ranger
 BugReports: https://github.com/imbs-hl/ranger/issues

NEWS.md

@@ -1,4 +1,7 @@
 
+# ranger 0.16.1
+* Allow vector min.node.size and min.bucket for class-specific limits
+
 # ranger 0.16.0
 * New CRAN version
 

R/ranger.R

@@ -109,8 +109,8 @@
 ##' @param importance Variable importance mode, one of 'none', 'impurity', 'impurity_corrected', 'permutation'. The 'impurity' measure is the Gini index for classification, the variance of the responses for regression and the sum of test statistics (see \code{splitrule}) for survival. 
 ##' @param write.forest Save \code{ranger.forest} object, required for prediction. Set to \code{FALSE} to reduce memory usage if no prediction intended.
 ##' @param probability Grow a probability forest as in Malley et al. (2012). 
-##' @param min.node.size Minimal node size to split at. Default 1 for classification, 5 for regression, 3 for survival, and 10 for probability.
-##' @param min.bucket Minimal terminal node size. No nodes smaller than this value can occur. Default 3 for survival and 1 for all other tree types. 
+##' @param min.node.size Minimal node size to split at. Default 1 for classification, 5 for regression, 3 for survival, and 10 for probability. For classification, this can be a vector of class-specific values. 
+##' @param min.bucket Minimal terminal node size. No nodes smaller than this value can occur. Default 3 for survival and 1 for all other tree types. For classification, this can be a vector of class-specific values. 
 ##' @param max.depth Maximal tree depth. A value of NULL or 0 (the default) corresponds to unlimited depth, 1 to tree stumps (1 split per tree).
 ##' @param replace Sample with replacement. 
 ##' @param sample.fraction Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement. For classification, this can be a vector of class-specific values. 
@@ -359,6 +359,15 @@ ranger <- function(formula = NULL, data = NULL, num.trees = 500, mtry = NULL,
     stop("Error: Unsupported type of dependent variable.")
   }
 
+  ## Number of levels
+  if (treetype %in% c(1, 9)) {
+    if (is.factor(y)) {
+      num_levels <- nlevels(y)
+    } else {
+      num_levels <- length(unique(y))
+    }
+  }
+
   ## Quantile prediction only for regression
   if (quantreg && treetype != 3) {
     stop("Error: Quantile prediction implemented only for regression outcomes.")
@@ -522,16 +531,46 @@ ranger <- function(formula = NULL, data = NULL, num.trees = 500, mtry = NULL,
   ## Minimum node size
   if (is.null(min.node.size)) {
     min.node.size <- 0
-  } else if (!is.numeric(min.node.size) || min.node.size < 0) {
-    stop("Error: Invalid value for min.node.size")
+  } else if (!is.numeric(min.node.size)) {
+    stop("Error: Invalid value for min.node.size.")
+  }
+  if (length(min.node.size) > 1) {
+    if (!(treetype %in% c(1, 9))) {
+      stop("Error: Invalid value for min.node.size. Vector values only valid for classification forests.")
+    }
+    if (any(min.node.size < 0)) {
+      stop("Error: Invalid value for min.node.size. Please give a nonnegative value or a vector of nonnegative values.")
+    }
+    if (length(min.node.size) != num_levels) {
+      stop("Error: Invalid value for min.node.size Expecting ", num_levels, " values, provided ", length(min.node.size), ".")
+    }
+  } else {
+    if (min.node.size < 0) {
+      stop("Error: Invalid value for min.node.size. Please give a nonnegative value or a vector of nonnegative values.")
+    }
   }
 
   ## Minimum bucket size
   if (is.null(min.bucket)) {
     min.bucket <- 0
-  } else if (!is.numeric(min.bucket) || min.bucket < 0) {
+  } else if (!is.numeric(min.bucket)) {
     stop("Error: Invalid value for min.bucket")
   }
+  if (length(min.bucket) > 1) {
+    if (!(treetype %in% c(1, 9))) {
+      stop("Error: Invalid value for min.bucket Vector values only valid for classification forests.")
+    }
+    if (any(min.bucket < 0)) {
+      stop("Error: Invalid value for min.bucket Please give a nonnegative value or a vector of nonnegative values.")
+    }
+    if (length(min.bucket) != num_levels) {
+      stop("Error: Invalid value for min.bucket Expecting ", num_levels, " values, provided ", length(min.bucket), ".")
+    }
+  } else {
+    if (min.bucket < 0) {
+      stop("Error: Invalid value for min.bucket Please give a nonnegative value or a vector of nonnegative values.")
+    }
+  }
 
   ## Tree depth
   if (is.null(max.depth)) {
@@ -554,8 +593,8 @@ ranger <- function(formula = NULL, data = NULL, num.trees = 500, mtry = NULL,
     if (sum(sample.fraction) <= 0) {
       stop("Error: Invalid value for sample.fraction. Sum of values must be >0.")
     }
-    if (length(sample.fraction) != nlevels(y)) {
-      stop("Error: Invalid value for sample.fraction. Expecting ", nlevels(y), " values, provided ", length(sample.fraction), ".")
+    if (length(sample.fraction) != num_levels) {
+      stop("Error: Invalid value for sample.fraction. Expecting ", num_levels, " values, provided ", length(sample.fraction), ".")
     }
     if (!replace & any(sample.fraction * length(y) > table(y))) {
       idx <- which(sample.fraction * length(y) > table(y))[1]

src/Forest.cpp

@@ -27,7 +27,7 @@
 namespace ranger {
 
 Forest::Forest() :
-    verbose_out(0), num_trees(DEFAULT_NUM_TREE), mtry(0), min_node_size(0), min_bucket(0), num_independent_variables(0), seed(0), num_samples(
+    verbose_out(0), num_trees(DEFAULT_NUM_TREE), mtry(0), min_node_size({0}), min_bucket({0}), num_independent_variables(0), seed(0), num_samples(
         0), prediction_mode(false), memory_mode(MEM_DOUBLE), sample_with_replacement(true), memory_saving_splitting(
         false), splitrule(DEFAULT_SPLITRULE), predict_all(false), keep_inbag(false), sample_fraction( { 1 }), holdout(
         false), prediction_type(DEFAULT_PREDICTIONTYPE), num_random_splits(DEFAULT_NUM_RANDOM_SPLITS), max_depth(
@@ -62,6 +62,9 @@ void Forest::initCpp(std::string dependent_variable_name, MemoryMode memory_mode
   if (!load_forest_filename.empty()) {
     prediction_mode = true;
   }
+
+  std::vector<uint> min_node_size_vector = { min_node_size };
+  std::vector<uint> min_bucket_vector = { min_bucket };
 
   // Sample fraction default and convert to vector
   if (sample_fraction == 0) {
@@ -79,7 +82,7 @@ void Forest::initCpp(std::string dependent_variable_name, MemoryMode memory_mode
 
   // Call other init function
   init(loadDataFromFile(input_file), mtry, output_prefix, num_trees, seed, num_threads, importance_mode,
-      min_node_size, min_bucket, prediction_mode, sample_with_replacement, unordered_variable_names, memory_saving_splitting,
+      min_node_size_vector, min_bucket_vector, prediction_mode, sample_with_replacement, unordered_variable_names, memory_saving_splitting,
       splitrule, predict_all, sample_fraction_vector, alpha, minprop, holdout, prediction_type, num_random_splits,
       false, max_depth, regularization_factor, regularization_usedepth, false);
 
@@ -133,7 +136,7 @@ void Forest::initCpp(std::string dependent_variable_name, MemoryMode memory_mode
 // #nocov end
 
 void Forest::initR(std::unique_ptr<Data> input_data, uint mtry, uint num_trees, std::ostream* verbose_out, uint seed,
-    uint num_threads, ImportanceMode importance_mode, uint min_node_size, uint min_bucket,
+    uint num_threads, ImportanceMode importance_mode, std::vector<uint>& min_node_size, std::vector<uint>& min_bucket,
     std::vector<std::vector<double>>& split_select_weights, const std::vector<std::string>& always_split_variable_names,
     bool prediction_mode, bool sample_with_replacement, const std::vector<std::string>& unordered_variable_names,
     bool memory_saving_splitting, SplitRule splitrule, std::vector<double>& case_weights,
@@ -178,7 +181,7 @@ void Forest::initR(std::unique_ptr<Data> input_data, uint mtry, uint num_trees,
 }
 
 void Forest::init(std::unique_ptr<Data> input_data, uint mtry, std::string output_prefix,
-    uint num_trees, uint seed, uint num_threads, ImportanceMode importance_mode, uint min_node_size, uint min_bucket,
+    uint num_trees, uint seed, uint num_threads, ImportanceMode importance_mode, std::vector<uint>& min_node_size, std::vector<uint>& min_bucket,
     bool prediction_mode, bool sample_with_replacement, const std::vector<std::string>& unordered_variable_names,
     bool memory_saving_splitting, SplitRule splitrule, bool predict_all, std::vector<double>& sample_fraction,
     double alpha, double minprop, bool holdout, PredictionType prediction_type, uint num_random_splits, bool order_snps,
@@ -323,7 +326,7 @@ void Forest::writeOutput() {
     *verbose_out << "Sample size:                       " << num_samples << std::endl;
     *verbose_out << "Number of independent variables:   " << num_independent_variables << std::endl;
     *verbose_out << "Mtry:                              " << mtry << std::endl;
-    *verbose_out << "Target node size:                  " << min_node_size << std::endl;
+    *verbose_out << "Target node size:                  " << min_node_size[0] << std::endl;
     *verbose_out << "Variable importance mode:          " << importance_mode << std::endl;
     *verbose_out << "Memory mode:                       " << memory_mode << std::endl;
     *verbose_out << "Seed:                              " << seed << std::endl;
@@ -473,7 +476,7 @@ void Forest::grow() {
     }
 
     trees[i]->init(data.get(), mtry, num_samples, tree_seed, &deterministic_varIDs, tree_split_select_weights,
-        importance_mode, min_node_size, min_bucket, sample_with_replacement, memory_saving_splitting, splitrule, &case_weights,
+        importance_mode, &min_node_size, &min_bucket, sample_with_replacement, memory_saving_splitting, splitrule, &case_weights,
         tree_manual_inbag, keep_inbag, &sample_fraction, alpha, minprop, holdout, num_random_splits, max_depth,
         &regularization_factor, regularization_usedepth, &split_varIDs_used, save_node_stats);
   }

src/Forest.h

@@ -48,7 +48,7 @@ class Forest {
       bool holdout, PredictionType prediction_type, uint num_random_splits, uint max_depth,
       const std::vector<double>& regularization_factor, bool regularization_usedepth);
   void initR(std::unique_ptr<Data> input_data, uint mtry, uint num_trees, std::ostream* verbose_out, uint seed,
-      uint num_threads, ImportanceMode importance_mode, uint min_node_size, uint min_bucket,
+      uint num_threads, ImportanceMode importance_mode, std::vector<uint>& min_node_size, std::vector<uint>& min_bucket,
       std::vector<std::vector<double>>& split_select_weights,
       const std::vector<std::string>& always_split_variable_names, bool prediction_mode, bool sample_with_replacement,
       const std::vector<std::string>& unordered_variable_names, bool memory_saving_splitting, SplitRule splitrule,
@@ -58,7 +58,7 @@ class Forest {
       const std::vector<double>& regularization_factor, bool regularization_usedepth,
       bool node_stats);
   void init(std::unique_ptr<Data> input_data, uint mtry, std::string output_prefix,
-      uint num_trees, uint seed, uint num_threads, ImportanceMode importance_mode, uint min_node_size, uint min_bucket,
+      uint num_trees, uint seed, uint num_threads, ImportanceMode importance_mode, std::vector<uint>& min_node_size, std::vector<uint>& min_bucket,
       bool prediction_mode, bool sample_with_replacement, const std::vector<std::string>& unordered_variable_names,
       bool memory_saving_splitting, SplitRule splitrule, bool predict_all, std::vector<double>& sample_fraction,
       double alpha, double minprop, bool holdout, PredictionType prediction_type, uint num_random_splits,
@@ -119,10 +119,10 @@ class Forest {
   uint getMtry() const {
     return mtry;
   }
-  uint getMinNodeSize() const {
+  const std::vector<uint>& getMinNodeSize() const {
     return min_node_size;
   }
-  uint getMinBucket() const {
+  const std::vector<uint>& getMinBucket() const {
     return min_bucket;
   }
   size_t getNumIndependentVariables() const {
@@ -209,8 +209,8 @@ class Forest {
   std::vector<std::string> dependent_variable_names; // time,status for survival
   size_t num_trees;
   uint mtry;
-  uint min_node_size;
-  uint min_bucket;
+  std::vector<uint> min_node_size;
+  std::vector<uint> min_bucket;
   size_t num_independent_variables;
   uint seed;
   size_t num_samples;

src/ForestClassification.cpp

@@ -54,13 +54,13 @@ void ForestClassification::initInternal() {
   }
 
   // Set minimal node size
-  if (min_node_size == 0) {
-    min_node_size = DEFAULT_MIN_NODE_SIZE_CLASSIFICATION;
+  if (min_node_size.size() == 1 && min_node_size[0] == 0) {
+    min_node_size[0] = DEFAULT_MIN_NODE_SIZE_CLASSIFICATION;
   }
 
   // Set minimal bucket size
-  if (min_bucket == 0) {
-    min_bucket = DEFAULT_MIN_BUCKET;
+  if (min_bucket.size() == 1 && min_bucket[0] == 0) {
+    min_bucket[0] = DEFAULT_MIN_BUCKET;
   }
 
   // Create class_values and response_classIDs

src/ForestProbability.cpp

@@ -59,13 +59,13 @@ void ForestProbability::initInternal() {
   }
 
   // Set minimal node size
-  if (min_node_size == 0) {
-    min_node_size = DEFAULT_MIN_NODE_SIZE_PROBABILITY;
+  if (min_node_size.size() == 1 && min_node_size[0] == 0) {
+    min_node_size[0] = DEFAULT_MIN_NODE_SIZE_PROBABILITY;
   }
 
   // Set minimal bucket size
-  if (min_bucket == 0) {
-    min_bucket = DEFAULT_MIN_BUCKET;
+  if (min_bucket.size() == 1 && min_bucket[0] == 0) {
+    min_bucket[0] = DEFAULT_MIN_BUCKET;
   }
 
   // Create class_values and response_classIDs

src/ForestRegression.cpp

@@ -48,13 +48,13 @@ void ForestRegression::initInternal() {
   }
 
   // Set minimal node size
-  if (min_node_size == 0) {
-    min_node_size = DEFAULT_MIN_NODE_SIZE_REGRESSION;
+  if (min_node_size.size() == 1 && min_node_size[0] == 0) {
+    min_node_size[0] = DEFAULT_MIN_NODE_SIZE_REGRESSION;
   }
 
   // Set minimal bucket size
-  if (min_bucket == 0) {
-    min_bucket = DEFAULT_MIN_BUCKET;
+  if (min_bucket.size() == 1 && min_bucket[0] == 0) {
+    min_bucket[0] = DEFAULT_MIN_BUCKET;
   }
 
   // Error if beta splitrule used with data outside of [0,1]

src/ForestSurvival.cpp

@@ -98,13 +98,13 @@ void ForestSurvival::initInternal() {
   }
 
   // Set minimal node size
-  if (min_node_size == 0) {
-    min_node_size = DEFAULT_MIN_NODE_SIZE_SURVIVAL;
+  if (min_node_size.size() == 1 && min_node_size[0] == 0) {
+    min_node_size[0] = DEFAULT_MIN_NODE_SIZE_SURVIVAL;
   }
 
   // Set minimal bucket size
-  if (min_bucket == 0) {
-    min_bucket = DEFAULT_MIN_BUCKET_SURVIVAL;
+  if (min_bucket.size() == 1 && min_bucket[0] == 0) {
+    min_bucket[0] = DEFAULT_MIN_BUCKET_SURVIVAL;
   }
 
   // Sort data if extratrees and not memory saving mode