allow steps of >1 in orsf_vs

DESCRIPTION

@@ -48,4 +48,4 @@ Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2

NEWS.md

@@ -1,4 +1,8 @@
-# aorsf 0.1.5 (unreleased)
+# aorsf 0.1.6 (unreleased)
+
+* added `n_predictor_drop` to `orsf_vs()`. Dropping one predictor at a time makes `orsf_vs()` slow for data with hundreds of predictors. Using a larger value for `n_predictor_drop` helps speed this up. The default value of `n_predictor_drop` is 1 to maintain backward compatibility.
+
+# aorsf 0.1.5 
 
 * fixed an issue where omitting NA values would cause an error in regression forests.
 

R/coerce_nans.R

@@ -1,13 +1,15 @@
+#' @noRd
 coerce_nans <- function(x, to){
  UseMethod('coerce_nans')
 }
 
+#' @noRd
 coerce_nans.list <- function(x, to){
 
  lapply(x, coerce_nans, to = to)
 
 }
-
+#' @noRd
 coerce_nans.factor <-
  coerce_nans.integer <-
  coerce_nans.double <-

R/orsf_R6.R

@@ -703,7 +703,9 @@ ObliqueForest <- R6::R6Class(
 
   # Variable selection
   # returns a data.table with variable selection info
-  select_variables = function(n_predictor_min, verbose_progress){
+  select_variables = function(n_predictor_min,
+                              n_predictor_drop,
+                              verbose_progress){
 
    public_state <- list(verbose_progress = self$verbose_progress,
                         forest           = self$forest,
@@ -712,7 +714,9 @@ ObliqueForest <- R6::R6Class(
    object_trained <- self$trained
 
    out <- try(
-    private$select_variables_internal(n_predictor_min, verbose_progress)
+    private$select_variables_internal(n_predictor_min,
+                                      n_predictor_drop,
+                                      verbose_progress)
    )
 
    private$restore_state(public_state, private_state = NULL)
@@ -2928,9 +2932,11 @@ ObliqueForest <- R6::R6Class(
 
   },
 
-  select_variables_internal = function(n_predictor_min, verbose_progress){
+  select_variables_internal = function(n_predictor_min,
+                                       n_predictor_drop,
+                                       verbose_progress){
 
-   n_predictors <- length(private$data_names$x_original)
+   n_predictors <- length(private$data_names$x_ref_code)
 
    # verbose progress on the forest should always be FALSE
    # because for orsf_vs, verbosity is coordinated in R
@@ -2941,7 +2947,7 @@ ObliqueForest <- R6::R6Class(
     stat_value = rep(NA_real_, n_predictors),
     variables_included = vector(mode = 'list', length = n_predictors),
     predictors_included = vector(mode = 'list', length = n_predictors),
-    predictor_dropped = rep(NA_character_, n_predictors)
+    predictor_dropped = vector(mode = 'list', length = n_predictors)
    )
 
    # if the forest was not trained prior to variable selection
@@ -3045,9 +3051,21 @@ ObliqueForest <- R6::R6Class(
     cpp_args$mtry <- mtry_safe
     cpp_output <- do.call(orsf_cpp, args = cpp_args)
 
-    worst_index <- which.min(cpp_output$importance)
-    worst_predictor <- colnames(cpp_args$x)[worst_index]
+    n_drop <- min(n_predictor_drop,
+                  n_predictors - n_predictor_min)
+
+    if(n_drop > 0){
+
+     worst_index <- order(cpp_output$importance)[seq(n_drop)]
+
+     worst_predictor <- colnames(cpp_args$x)[worst_index]
 
+    } else {
+
+     worst_predictor <- NA_character_
+     n_drop <- 1
+
+    }
 
     .variables_included <- with(
      variable_key,
@@ -3062,8 +3080,8 @@ ObliqueForest <- R6::R6Class(
                   predictor_dropped = worst_predictor)]
 
     cpp_args$x <- cpp_args$x[, -worst_index, drop = FALSE]
-    n_predictors <- n_predictors - 1
-    current_progress <- current_progress + 1
+    n_predictors <- n_predictors - n_drop
+    current_progress <- current_progress + n_drop
 
    }
 

R/orsf_data_prep.R

@@ -1,8 +1,9 @@
-
+#' @noRd
 orsf_data_prep <- function(data, ...){
  UseMethod('orsf_data_prep')
 }
 
+#' @noRd
 orsf_data_prep.list <- function(data, ...){
 
  lengths <- vapply(data, length, integer(1))
@@ -43,12 +44,14 @@ orsf_data_prep.list <- function(data, ...){
 
 }
 
+#' @noRd
 orsf_data_prep.recipe <- function(data, ...){
 
  getElement(data, 'template')
 
 }
 
+#' @noRd
 orsf_data_prep.data.frame <- function(data, ...){
  data
 }

R/orsf_vs.R

@@ -3,6 +3,7 @@
 #'
 #' @inheritParams predict.ObliqueForest
 #' @param n_predictor_min (*integer*) the minimum number of predictors allowed
+#' @param n_predictor_drop (*integer*) the number of predictors dropped at each step
 #' @param verbose_progress (*logical*) not implemented yet. Should progress be printed to the console?
 #'
 #' @return a [data.table][data.table::data.table-package] with four columns:
@@ -38,8 +39,15 @@
 
 orsf_vs <- function(object,
                     n_predictor_min = 3,
+                    n_predictor_drop = 1,
                     verbose_progress = NULL){
 
+ if(object$importance_type == 'none'){
+  stop("object must be specified with importance",
+       "of 'anova', 'negate', or 'permute'",
+       call. = FALSE)
+ }
+
  check_arg_is(arg_value = object,
               arg_name = 'object',
               expected_class = 'ObliqueForest')
@@ -55,6 +63,14 @@ orsf_vs <- function(object,
                 arg_name = 'n_predictor_min',
                 bound = 1)
 
+
+ check_arg_type(arg_value = n_predictor_drop,
+                arg_name = 'n_predictor_drop',
+                expected_type = 'numeric')
+
+ check_arg_is_integer(arg_value = n_predictor_drop,
+                      arg_name = 'n_predictor_drop')
+
  check_arg_lt(arg_value = n_predictor_min,
               arg_name = 'n_predictor_min',
               bound = length(object$get_names_x()),
@@ -74,7 +90,9 @@ orsf_vs <- function(object,
                   arg_name = 'verbose_progress',
                   expected_length = 1)
 
- object$select_variables(n_predictor_min, verbose_progress)
+ object$select_variables(n_predictor_min,
+                         n_predictor_drop,
+                         verbose_progress)
 
 }