Many improvements to ablation.

NEWS.md

@@ -11,6 +11,11 @@
  * Now `blockSize` multiplies `elitistNewInstances`, which should ensure that
    irace evaluates instances in blocks.
 
+ * Several improvements in `ablation()` may result in different ablation
+   results using this version of irace. Also, the log generated by `ablation()`
+   has some important changes to record more detailed information and it is
+   more consistent with the log generated by `irace()`.
+
 ## New features and improvements
 
  * When `targetRunner` or `targetEvaluator` are implemented in R, using random
@@ -28,6 +33,14 @@
  * `getConfigurationById()` now returns configurations in the same order
    (including repeated values) of the IDs passed as argument.
 
+ * `ablation(type="full")` will evaluate all intermediate configurations on all
+   instances at once.  This makes better use of parallel resources but will not
+   report any information about progress after each instance.
+
+ * `ablation()` now works better with dependent-domain parameters and with
+   complicated conditions.  Ablation will not generate intermediate
+   configurations that are forbidden.
+
 ## Fixes
 
  * Fix #76: Recovery (`--recover-file`) is working again with a completely new implementation.

R/generation.R

@@ -27,6 +27,15 @@ repairConfigurations <- function(x, parameters, repair)
   x
 }
 
+is_within_dependent_bound <- function(param, configuration, value)
+{
+  domain <- unlist(lapply(param[["domain"]], eval, configuration))
+  # Value gets truncated (defined from robotics initial requirements)
+  if (param[["type"]] == "i")
+    domain <- as.integer(domain)
+  domain[[1L]] <= value && value <= domain[[2L]]
+}
+
 ## Calculates the parameter bounds when parameters domain is dependent
 getDependentBound <- function(param, configuration)
 {
@@ -87,12 +96,12 @@ generate_sobol <- function(parameters, n, repair = NULL)
   # FIXME: How to do this faster using data.table?
   for (x in nodep_names)
     set(confs, j = x, value = param_quantile(parameters$get(x), confs[[x]]))
-  
+
   for (x in parameters$names_fixed)
     set(confs, j = x, value = parameters$domains[[x]])
-  
+
   setcolorder(confs, parameters$names)
-  
+
   max_level <- max(hierarchy)
   if (max_level > 1L) {
     .NEWVALUE <- .DOMAIN <- NULL # To silence CRAN warnings.
@@ -136,7 +145,7 @@ sampleSobol <- function(parameters, n, repair = NULL)
 {
   newConfigurations <- generate_sobol(parameters, n, repair)
   newConfigurations <- unique(newConfigurations)
-  forbidden <- parameters$forbidden  
+  forbidden <- parameters$forbidden
   newConfigurations <- filter_forbidden(newConfigurations, forbidden)
   have <- nrow(newConfigurations)
   if (have < n) {
@@ -190,7 +199,7 @@ generate_uniform <- function(parameters, nbConfigurations, repair = NULL)
 sampleUniform <- function(parameters, nbConfigurations, repair = NULL)
 {
   newConfigurations <- generate_uniform(parameters, nbConfigurations, repair)
-  forbidden <- parameters$forbidden  
+  forbidden <- parameters$forbidden
   if (!is.null(forbidden)) {
     retries <- 100L
     repeat {
@@ -219,7 +228,7 @@ sample_from_model <- function(parameters, eliteConfigurations, model,
     print(utils::str(ids_elites))
     print(utils::str(eliteConfigurations[[".ID."]]))
   })
-  
+
   newConfigurations  <- configurations_alloc(parameters$names, nrow = nbNewConfigurations, parameters)
   idx_elites <- sample.int(n = length(ids_elites), size = nbNewConfigurations,
                            prob = eliteConfigurations[[".WEIGHT."]], replace = TRUE)
@@ -286,11 +295,3 @@ sampleModel <- function(parameters, eliteConfigurations, model,
   }
   newConfigurations
 }
-
-
-
-
-
-
-
-

devel-examples/vignette-example/create-example-file.R

@@ -22,12 +22,11 @@ parameters <- readParameters(text = parameters_table)
 ## configuration on a single instance. For simplicity, we restrict to
 ## three-dimensional functions and we set the maximum number of
 ## iterations of SANN to 5000.
-
 target_runner <- function(experiment, scenario)
 {
   instance <- experiment$instance
   configuration <- experiment$configuration
-  D <- 3
+  D <- 3L
   par <- runif(D, min=-1, max=1)
   fn <- function(x) {
     # Functions to be optimized:
@@ -39,7 +38,7 @@ target_runner <- function(experiment, scenario)
     }
     f_rastrigin <- function (x)
       sum(x * x - 10 * cos(2 * pi * x) + 10)
-    
+
     (instance * f_rastrigin(x) + (1 - instance) * f_rosenbrock(x))
   }
   res <- stats::optim(par,fn, method="SANN",
@@ -79,10 +78,6 @@ save(iraceResults, file="sann.rda", version = 3L)
 
 ### ACOTSP example
 
-iracebin <- system.file(package="irace", "bin/irace")
-if (0 != file.access(iracebin, mode=1))
-  stop("Error: ", iracebin, " is not executable or not found!")
-
 download_uncompress <- function(url, exdir, flat = FALSE) {
   exts <- c(".tar.bz2", ".tar.gz", ".tgz", ".tar.xz", ".zip")
   fileext <- exts[endsWith(url, exts)]
@@ -132,11 +127,14 @@ setup_acotsp <- function() {
 setup_tsp_rue_2000()
 setup_acotsp()
 
+iracebin <- system.file(package="irace", "bin/irace")
+if (0 != file.access(iracebin, mode=1))
+  stop("Error: ", iracebin, " is not executable or not found!")
 system(paste0("nice -n 19 ", iracebin, " --parallel 2 | tee irace-acotsp-stdout.txt 2>&1"))
 
 # Create log-ablation.Rdata
 cat('**** Running ablation("irace-acotsp.Rdata")\n')
-ablation("irace-acotsp.Rdata", parallel = 1)
+ablation("irace-acotsp.Rdata", parallel = 2L)
 
 iraceResults <- read_logfile("irace-acotsp.Rdata")
 
@@ -171,7 +169,7 @@ experiment <- list (
   id_instance      = res[["instanceID"]],
   seed             = res[["seed"]],
   configuration    = getConfigurationById(iraceResults, 1L, drop.metadata = TRUE),
-  instance         = res[["instance"]], 
+  instance         = res[["instance"]],
   switches         = iraceResults$scenario$parameters$switches)
 
 # Output
@@ -182,6 +180,3 @@ output <- list(
 
 # save in the folder
 save(experiment, output, file="examples.Rdata", version = 3L)
-
-
-