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
Gaussian Processes in MGCV
I lay out the canonical GP interpretation of MGCV’s GAM parameters here. Prof. Wood updated the package with stationary GP smooths after a request. I’ve run through the predict.gam source code in a debugger, and mainly, the computation of predictions follows:
Dead Code & Syntax Trees
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.
Random Projects
Random Projects
2020
Astrophotography
I used to do a fair bit of astrophotography in university - it’s harder to find good skies now living in the city. Here are some of my old pictures. I’ve kept making rookie mistakes (too much ISO, not much exposure time, using a slow lens, bad stacking, …), for that I apologize!
Probabilistic PCA
I’ve been reading about PPCA, and this post summarizes my understanding of it. I took a lot of this from Pattern Recognition and Machine Learning by Bishop.
Modelling with Spotify Data
The main objective of this post was just to write about my typical workflow and views rather than come up with a great model. The structure of this data is also outside my immediate domain so I thought it’d be fun to write up a small diary on making a model with it.
Random Stuff
Random Stuff
Morphing with GPs
The main aim here was to morph space inside a square but such that the transformation preserves some kind of ordering of the points. I wanted to use it to generate some random graphs on a flat surface and introduce spatial deformation to make the graphs more interesting.
SEIR Models
I had a go at a few SEIR models, this is a rough diary of the process.
Speech Synthesis
The initial aim here was to model speech samples as realizations of a Gaussian process with some appropriate covariance function, by conditioning on the spectrogram. I fit a spectral mixture kernel to segments of audio data and concatenated the segments to obtain the full waveform. Partway into writing efficient sampling code (generating waveforms using the Gaussian process state space representation), I realized that it’s actually quite easy to obtain waveforms if you’ve already got a spectrogram.
Efficient Gaussian Process Computation
I’ll try to give examples of efficient gaussian process computation here, like the vec trick (Kronecker product trick), efficient toeliptz and circulant matrix computations, RTS smoothing and Kalman filtering using state space representations, and so on.
Sparse Gaussian Processes
Minimal Working Examples
2019
Gaussian Process Middle C
First of my experiments on audio modeling using Gaussian processes. Here, I construct a GP that, when sampled, plays middle c the way a grand piano would.
An Ising-Like Model
… using Stan & HMC
Stochastic Bernoulli Probabilities
Consider: