run_experiment {CAISEr}  R Documentation 
Design and run a full experiment  calculate the required number of
instances, run the algorithms on each problem instance using the iterative
approach based on optimal sample size ratios, and return the results of the
experiment. This routine builds upon calc_instances()
and calc_nreps()
,
so refer to the documentation of these two functions for details.
run_experiment(
instances,
algorithms,
d,
se.max,
power = 0.8,
sig.level = 0.05,
power.target = "mean",
dif = "simple",
comparisons = "all.vs.all",
alternative = "two.sided",
test = "t.test",
method = "param",
nstart = 20,
nmax = 100 * length(algorithms),
force.balanced = FALSE,
ncpus = 2,
boot.R = 499,
seed = NULL,
save.partial.results = NA,
load.partial.results = NA,
save.final.result = NA
)
instances 
list object containing the definitions of the
available instances. This list may (or may not) be exhausted in the
experiment. To estimate the number of required instances,
see 
algorithms 
a list object containing the definitions of all algorithms.
See Section 
d 
minimally relevant effect size (MRES), expressed as a standardized
effect size, i.e., "deviation from H0" / "standard deviation".
See 
se.max 
desired upper limit for the standard error of the estimated
difference between pairs of algorithms. See Section

power 
(desired) test power. See 
sig.level 
familywise significance level (alpha) for the experiment.
See 
power.target 
which comparison should have the desired 
dif 
type of difference to be used. Accepts "perc" (for percent differences) or "simple" (for simple differences) 
comparisons 
type of comparisons being performed. Accepts "all.vs.first"
(in which cases the first object in 
alternative 
type of alternative hypothesis ("two.sided" or
"less" or "greater"). See 
test 
type of test to be used ("t.test", "wilcoxon" or "binomial") 
method 
method to use for estimating the standard errors. Accepts "param" (for parametric) or "boot" (for bootstrap) 
nstart 
initial number of algorithm runs for each algorithm.
See Section 
nmax 
maximum number of runs to execute on each instance (see

force.balanced 
logical flag to force the use of balanced sampling for the algorithms on each instance 
ncpus 
number of cores to use 
boot.R 
number of bootstrap resamples to use (if 
seed 
seed for the random number generator 
save.partial.results 
should partial results be saved to files? Can be
either 
load.partial.results 
should partial results be loaded from files? Can
be either 
save.final.result 
should the final results be saved to file? Can be
either 
a list object containing the following fields:
Configuration
 the full input configuration (for reproducibility)
data.raw
 data frame containing all observations generated
data.summary
 data frame summarizing the experiment.
N
 number of instances sampled
N.star
 number of instances required
total.runs
 total number of algorithm runs performed
instances.sampled
 names of the instances sampled
Underpowered
 flag: TRUE if N < N.star
Parameter instances
must contain a list of instance objects, where
each field is itself a list, as defined in the documentation of function
calc_nreps()
. In short, each element of instances
is an instance
, i.e.,
a named list containing all relevant parameters that define the problem
instance. This list must contain at least the field instance$FUN
, with the
name of the problem instance function, that is, a routine that calculates
y = f(x). If the instance requires additional parameters, these must also be
provided as named fields.
An additional field, "instance$alias", can be used to provide the instance
with a unique identifier (e.g., when using an instance generator).
Object algorithms
is a list in which each component is a named
list containing all relevant parameters that define an algorithm to be
applied for solving the problem instance. In what follows algorithms[[k]]
refers to any algorithm specified in the algorithms
list.
algorithms[[k]]
must contain an algorithms[[k]]$FUN
field, which is a
character object with the name of the function that calls the algorithm; as
well as any other elements/parameters that algorithms[[k]]$FUN
requires
(e.g., stop criteria, operator names and parameters, etc.).
The function defined by the routine algorithms[[k]]$FUN
must have the
following structure: supposing that the list in algorithms[[k]]
has
fields algorithm[[k]]$FUN = "myalgo"
, algorithms[[k]]$par1 = "a"
and
algorithms[[k]]$par2 = 5
, then:
myalgo < function(par1, par2, instance, ...){ # # <do stuff> # return(results) }
That is, it must be able to run if called as:
# remove '$FUN' and '$alias' field from list of arguments # and include the problem definition as field 'instance' myargs < algorithm[names(algorithm) != "FUN"] myargs < myargs[names(myargs) != "alias"] myargs$instance < instance # call function do.call(algorithm$FUN, args = myargs)
The algorithm$FUN
routine must return a list containing (at
least) the performance value of the final solution obtained, in a field named
value
(e.g., result$value
) after a given run. In general it is easier to
write a small wrapper function around existing implementations.
In the general case the initial number of observations / algorithm /
instance (nstart
) should be relatively high. For the parametric case
we recommend 10~15 if outliers are not expected, and 30~40 (at least) if that
assumption cannot be made. For the bootstrap approach we recommend using at
least 15 or 20. However, if some distributional assumptions can be
made  particularly low skewness of the population of algorithm results on
the test instances), then nstart
can in principle be as small as 5 (if the
output of the algorithm were known to be normal, it could be 1).
In general, higher sample sizes are the price to pay for abandoning
distributional assumptions. Use lower values of nstart
with caution.
Parameter dif
informs the type of difference in performance to be used
for the estimation (\mu_a
and \mu_b
represent the mean
performance of any two algorithms on the test instance, and mu
represents the grand mean of all algorithms given in algorithms
):
If dif == "perc"
and comparisons == "all.vs.first"
, the estimated
quantity is:
\phi_{1b} = (\mu_1  \mu_b) / \mu_1 = 1  (\mu_b / \mu_1)
.
If dif == "perc"
and comparisons == "all.vs.all"
, the estimated
quantity is:
\phi_{ab} = (\mu_a  \mu_b) / \mu
.
If dif == "simple"
it estimates \mu_a  \mu_b
.
If the parameter “ is set to either Wilcoxon
or 'Binomial', this
routine approximates the number of instances using the ARE of these tests
in relation to the paired t.test:
n.wilcox = n.ttest / 0.86 = 1.163 * n.ttest
n.binom = n.ttest / 0.637 = 1.570 * n.ttest
Felipe Campelo (fcampelo@ufmg.br, f.campelo@aston.ac.uk)
F. Campelo, F. Takahashi: Sample size estimation for power and accuracy in the experimental comparison of algorithms. Journal of Heuristics 25(2):305338, 2019.
P. Mathews. Sample size calculations: Practical methods for engineers and scientists. Mathews Malnar and Bailey, 2010.
A.C. Davison, D.V. Hinkley: Bootstrap methods and their application. Cambridge University Press (1997)
E.C. Fieller: Some problems in interval estimation. Journal of the Royal Statistical Society. Series B (Methodological) 16(2), 175–185 (1954)
V. Franz: Ratios: A short guide to confidence limits and proper use (2007). https://arxiv.org/pdf/0710.2024v1.pdf
D.C. Montgomery, C.G. Runger: Applied Statistics and Probability for Engineers, 6th ed. Wiley (2013)
D.J. Sheskin: Handbook of Parametric and Nonparametric Statistical Procedures, 4th ed., Chapman & Hall/CRC, 1996.
# Example using four dummy algorithms and 100 dummy instances.
# See [dummyalgo()] and [dummyinstance()] for details.
# Generating 4 dummy algorithms here, with means 15, 10, 30, 15 and standard
# deviations 2, 4, 6, 8.
algorithms < mapply(FUN = function(i, m, s){
list(FUN = "dummyalgo",
alias = paste0("algo", i),
distribution.fun = "rnorm",
distribution.pars = list(mean = m, sd = s))},
i = c(alg1 = 1, alg2 = 2, alg3 = 3, alg4 = 4),
m = c(15, 10, 30, 15),
s = c(2, 4, 6, 8),
SIMPLIFY = FALSE)
# Generate 100 dummy instances with centered exponential distributions
instances < lapply(1:100,
function(i) {rate < runif(1, 1, 10)
list(FUN = "dummyinstance",
alias = paste0("Inst.", i),
distr = "rexp", rate = rate,
bias = 1 / rate)})
my.results < run_experiment(instances, algorithms,
d = .5, se.max = .1,
power = .9, sig.level = .05,
power.target = "mean",
dif = "perc", comparisons = "all.vs.all",
ncpus = 1, seed = 1234)
# Take a look at the results
summary(my.results)
plot(my.results)