The GSgalgoR package provides a practical but straightforward callback mechanism
for adapting different galgo() execution sections to final user needs. The
GSgalgoR callbacks mechanism enables adding custom functions to change the
galgo() function behavior by including minor modification to galgo’s workflow.
A common application of the callback mechanism is to implement personalized
reports, saving partial information during the evolution process or compute the
execution time.
There are five possible points where the user can hook its own code inside
galgo() execution process.
galgo() execution process. (i.e. when galgo()
is about to start.)galgo() execution process. (i.e. when galgo() is about
to finish. )Each one of the five possible hooks can be accessed through parameters
with the *_callback* suffix in the galgo() function.
galgo(...,
start_galgo_callback = callback_default,# `galgo()` is about to start.
end_galgo_callback = callback_default, # `galgo()` is about to finish.
start_gen_callback = callback_default, # At the beginning of each generation
end_gen_callback = callback_default, # At the end of each generation
report_callback = callback_default, # In the middle of the generation,
# right after the new mating pool
# have been created.
...) A callback function definition can be any R function accepting six parameters.
-userdir: the directory (“character”) where the user can save information
into local filesystem.
-generation: the number (“integer”) of the current generation/iteration.
-pop_pool: the data.frame containing the resulting solutions for current
iteration.
-pareto: the solutions found by galgo() accross all generations in the
solution space
-prob_matrix: the expression set (“matrix) where features are rows and
samples distributed in columns.
-current_time: The current time (an object of class”POSIXct").
The following callback function example prints the generation number and current time every two iterations
library(GSgalgoR)
my_callback <-
function(userdir = "",
generation,
pop_pool,
pareto,
prob_matrix,
current_time) {
# code starts here
if (generation%%2 == 0)
message(paste0("generation: ",generation,
" current_time: ",current_time))
}then, the my_callback() function needs to be assigned to some of the
available hooks provided by the galgo(). An example of such assignment and
the resulting output is provided in the two snippets below.
A reduced version of the
TRANSBIG
dataset is used to setup the expression and clinical information
required for the galgo() function.
library(breastCancerTRANSBIG)data(transbig)
train <- transbig
rm(transbig)
expression <- Biobase::exprs(train)
clinical <- Biobase::pData(train)
OS <- survival::Surv(time = clinical$t.rfs, event = clinical$e.rfs)
# use a reduced dataset for the example
expression <- expression[sample(1:nrow(expression), 100), ]
# scale the expression matrix
expression <- t(scale(t(expression)))Then, the galgo() function is invoked and the recently defined function
my_callback() is assigned to the report_callback hook-point.
library(GSgalgoR)# Running galgo
GSgalgoR::galgo(generations = 6,
population = 15,
prob_matrix = expression,
OS = OS,
start_galgo_callback = GSgalgoR::callback_default,
end_galgo_callback = GSgalgoR::callback_default,
report_callback = my_callback, # call `my_callback()` in the mile
# of each generation/iteration.
start_gen_callback = GSgalgoR::callback_default,
end_gen_callback = GSgalgoR::callback_default)
#> Using CPU for computing pearson distance
#> generation: 2 current_time: 2022-04-26 16:50:57
#> generation: 4 current_time: 2022-04-26 16:50:59
#> generation: 6 current_time: 2022-04-26 16:51:00
#> NULLThe following callback function save in a temporary directory the solutions
obtained every five generation/iteration. A file the number of the generation
and with a rda. extension will be left in a directory defined
by the tempdir() function.
my_save_pop_callback <-
function(userdir = "",
generation,
pop_pool,
pareto,
prob_matrix,
current_time) {
directory <- paste0(tempdir(), "/")
if (!dir.exists(directory)) {
dir.create(directory, recursive = TRUE)
}
filename <- paste0(directory, generation, ".rda")
if (generation%%2 == 0){
save(file = filename, pop_pool)
}
message(paste("solution file saved in",filename))
}As usual, the galgo() function is invoked and the recently defined
function my_save_pop_callback() is assigned to the end_gen_callback
hook-point. As a result, every five generation/iteration the complete
solution obtained by galgo will be saved in a file.
# Running galgo
GSgalgoR::galgo(
generations = 6,
population = 15,
prob_matrix = expression,
OS = OS,
start_galgo_callback = GSgalgoR::callback_default,
end_galgo_callback = GSgalgoR::callback_default,
report_callback = my_callback,# call `my_callback()`
# in the middle of each generation/iteration.
start_gen_callback = GSgalgoR::callback_default,
end_gen_callback = my_save_pop_callback # call `my_save_pop_callback()`
# at the end of each
# generation/iteration
)
#> Using CPU for computing pearson distance
#> solution file saved in /tmp/RtmpiP5gb7/1.rda
#> generation: 2 current_time: 2022-04-26 16:51:07
#> solution file saved in /tmp/RtmpiP5gb7/2.rda
#> solution file saved in /tmp/RtmpiP5gb7/3.rda
#> generation: 4 current_time: 2022-04-26 16:51:09
#> solution file saved in /tmp/RtmpiP5gb7/4.rda
#> solution file saved in /tmp/RtmpiP5gb7/5.rda
#> generation: 6 current_time: 2022-04-26 16:51:10
#> solution file saved in /tmp/RtmpiP5gb7/6.rda
#> NULLBy default, GSfalgoR implements four callback functions
callback_default() a simple callback that does nothing at all. It is just
used for setting the default behavior of some of the hook-points
inside galgo()
callback_base_report() a report callback for printing basic information
about the solution provided by galgo() such as fitness and crowding distance.
callback_no_report() a report callback for informing the user galgo is
running. Not valuable information is shown.
callback_base_return_pop() a callback function for building and returning t
he galgo.Obj object.
In the the default definition of the galgo() function the hook-points are
defined as follow:
-start_galgo_callback = callback_default
-end_galgo_callback = callback_base_return_pop
-report_callback = callback_base_report
-start_gen_callback = callback_default
-end_gen_callback = callback_default
Notice by using the callback mechanism it is possible to modify even the
returning value of the galgo() function. The default
callback_base_return_pop() returns a galgo.Obj object, however it would
simple to change that behavior for something like the my_save_pop_callback()
and the function will not returning any value.
# Running galgo
GSgalgoR::galgo(
generations = 6,
population = 15,
prob_matrix = expression,
OS = OS,
start_galgo_callback = GSgalgoR::callback_default,
end_galgo_callback = my_save_pop_callback,
report_callback = my_callback, # call `my_callback()`
# in the middle of each generation/iteration
start_gen_callback = GSgalgoR::callback_default,
end_gen_callback = GSgalgoR::callback_default
)
#> Using CPU for computing pearson distance
#> generation: 2 current_time: 2022-04-26 16:51:17
#> generation: 4 current_time: 2022-04-26 16:51:19
#> generation: 6 current_time: 2022-04-26 16:51:20
#> solution file saved in /tmp/RtmpiP5gb7/6.rdaFor preserving the return behavior of the galgo() function,
callback_base_return_pop() should be called inside a custom callback.
An example of such situation is shown below:
another_callback <-
function(userdir = "",
generation,
pop_pool,
pareto,
prob_matrix,
current_time) {
# code starts here
# code ends here
callback_base_return_pop(userdir,
generation,
pop_pool,
prob_matrix,
current_time)
}sessionInfo()
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#> [1] survminer_0.4.9 ggpubr_0.4.0
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