This post was motivated by some R code that I came across (over a thousand lines of it) with a bunch of if-statements that were never called. I wanted an automatic way to get a minimal reproducing example of a test from this file. While reading about how to do this, I came across Dead Code Elimination, which kills unused and unreachable code and variables as an example.
An R library exists for code optimization but it didn’t address my issue of removing if statements by running through the code. So, to learn a bit about this, I made a naive attempt. It was more challenging and ugly that I thought it’d be.
My attempt was as follows - I break up the code into statements and check if a statement is an if statement, which has the following syntax: if (condition) expression_true else expression_false. I then evaluate the condition and replace the statement with the appropriate expression. Identifying how to play around with the syntax trees was challenging at first, but I noticed that as.list(quote(...)) breaks up ... into a list of expressions representing the syntax tree (noticed this in the pryr source!). This is obviously so much cleaner than regex but I still had to write a lot of special cases, e.g. handling NULLs, inline if statements, etc. I recursively split expressions, evaluate them and then recombine the lists into a call. Loops are ignored. A lot of work can still be done on this, but dunno if it’s worth the time.
Demo: R Code
contains_if <- function(ex_list) {
if(length(ex_list) <= 1)
return(FALSE)
else if(ex_list[[1]] == quote(`if`))
return(TRUE)
else {
return(any(sapply(as.list(ex_list), contains_if)))
}
}
check_for_inline_if <- function(ex_list) {
if(length(ex_list) == 3)
if(ex_list[[1]] == quote(`<-`))
if(length(as.list(ex_list[[3]])) >= 1)
if(as.list(ex_list[[3]])[[1]] == quote(`if`))
return(TRUE)
return(FALSE)
}
fix_inline_if <- function(ex_list) {
if_statement <- as.list(ex_list[[3]])
my_list <- c(ex_list[1:2], if_statement[3:4])
if_statement[[3]] <- as.call(my_list[c(1, 2, 3)])
if_statement[[4]] <- as.call(my_list[c(1, 2, 4)])
return(if_statement)
}
remove_unused_ifs <- function(expr) {
ex_list <- as.list(expr)
if(check_for_inline_if(ex_list)) {
ex_list <- fix_inline_if(ex_list)
expr <- as.call(ex_list)
}
if(length(expr) == 1) {
return(expr)
} else if(!contains_if(ex_list) |
ex_list[[1]] == quote(`for`) |
ex_list[[1]] == quote(`while`)) {
eval(expr, .GlobalEnv)
return(expr)
} else if(ex_list[[1]] == quote(`if`)) {
condition <- ex_list[[2]]
expr_true <- ex_list[[3]]
expr_flse <- if(length(ex_list) == 4)
ex_list[[4]] else quote({})
expr <- if(eval(condition, .GlobalEnv))
expr_true else expr_flse
return(remove_unused_ifs(expr))
} else {
return(lapply(ex_list, remove_unused_ifs))
}
}
recombine <- function(ex_list) {
if(is.list(ex_list)) {
if(any(sapply(ex_list, is.list))) {
return(recombine(lapply(ex_list, recombine)))
} else {
return(as.call(ex_list))
}
} else {
return(ex_list)
}
}
recombine(remove_unused_ifs(body(function() {
my_list <- list(my_bool = F)
abc <- if(TRUE) 1 else NULL
print(abc)
if(my_list$my_bool) {
print('hello_a')
} else {
if(!my_list$my_bool) {
if(TRUE) print('hello_b')
} else print('bye')
}
})))
# Output:
# {
# my_list <- list(my_bool = F)
# abc <- 1
# print(abc)
# {
# {
# print("hello_b")
# }
# }
# }2021
Efficient Gaussian Process Computation
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This post was motivated by some R code that I came across (over a thousand lines of it) with a bunch of if-statements that were never called. I wanted an automatic way to get a minimal reproducing example of a test from this file. While reading about how to do this, I came across Dead Code Elimination, which kills unused and unreachable code and variables as an example.
2020
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Random Stuff
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Sparse Gaussian Processes
Minimal Working Examples
2019
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An Ising-Like Model
… using Stan & HMC
Stochastic Bernoulli Probabilities
Consider: