How to optimize memory usage in dplyr + purrr - r

Question

I have an issue where after replicating data for a training and testing set, I'm showing a large amount of memory allocated to my user in Rstudio, but not being used in my R session. I've created a small example to reproduce my situation :)
This code runs a bunch of model, based on different formulas, algorithms, and parameter sets that I give it. It is a function, but I've created a simple script for reprex.
library(dplyr)
library(purrr)
library(modelr)
library(tidyr)
library(pryr)
# set my inputs
data <- mtcars
formulas <- c(test1 = mpg ~ cyl + wt + hp,
test2 = mpg ~ cyl + wt)
params = list()
methods <- "lm"
n <- 20 # num of cv splits
mult <- 10 # number of times I want to replicate some of the data
frac <- .25 # how much I want to cut down other data (fractional)
### the next few chunks get the unique combos of the inputs.
if (length(params) != 0) {
cross_params <- params %>%
map(cross) %>%
map_df(enframe, name = "param_set", .id = "method") %>%
list
} else cross_params <- NULL
methods_df <- tibble(method = methods) %>%
list %>%
append(cross_params) %>%
reduce(left_join, by = "method") %>%
split(1:nrow(.))
# wrangle formulas into a split dataframe
formulas_df <- tibble(formula = formulas,
name = names(formulas)) %>%
split(.$name)
# split out the data into n random train-test combos
cv_data <- data %>%
crossv_kfold(n) %>% # rsample?
mutate_at(vars(train:test), ~map(.x, as_tibble))
# sample out if needed
cv_data_samp <- cv_data %>%
mutate(train = modify(train,
~ .x %>%
split(.$gear == 4) %>%
# take a sample of the non-vo data
modify_at("FALSE", sample_frac, frac) %>%
# multiply out the vo-on data
modify_at("TRUE", function(.df) {
map_df(seq_along(1:mult), ~ .df)
}) %>%
bind_rows))
# get all unique combos of formula and method
model_combos <- list(cv = list(cv_data_samp),
form = formulas_df,
meth = methods_df) %>%
cross %>%
map_df(~ bind_cols(nest(.x$cv), .x$form, .x$meth)) %>%
unnest(data, .preserve = matches("formula|param|value")) %>%
{if ("value" %in% names(.)) . else mutate(., value = list(NULL))}
# run the models
model_combos %>%
# put all arguments into a single params column
mutate(params = pmap(list(formula = formula, data = train), list)) %>%
mutate(params = map2(params, value, ~ append(.x, .y))) %>%
mutate(params = modify(params, discard, is.null)) %>%
# run the models
mutate(model = invoke_map(method, params))
mem_change(rm(data, cv_data, cv_data_samp))
mem_used()
Now after I do this, my mem_used comes out to 77.3mb, but I'm seeing roughly double that (160Mb) allocated to my R user. This really explodes when my data is 3 Gb, which is my real-life case. I end up using 100Gb and tying up a whole server :(.
What is going on and how can I optimize?
Any help appreciated!!!

Answer 1

I figured this out! The issue was that I was converting my series of modelr resample objects to tibbles and that was exploding the memory even though I was subsequently sampling them down. The solution? Write methods for dealing with resample objects so that I never have to convert resample objects to tibble. These looked like:
# this function just samples the indexes instead of the data
sample_frac.resample <- function(data, frac) {
data$idx <- sample(data$idx, frac * length(data$idx))
data
}
# this function replicates the indexes. I should probably call it something else.
augment.resample <- function(data, n) {
data$idx <- unlist(map(seq_along(1:n), ~ data$idx))
data
}
# This function does simple splitting (logical only) of resample obejcts
split.resample <- function(data, .p) {
pos <- list(data = data$data, idx = which(.p, 1:nrow(data$data)))
neg <- list(data = data$data, idx = which(!.p, 1:nrow(data$data)))
class(pos) <- "resample"
class(neg) <- "resample"
list("TRUE" = pos,
"FALSE" = neg)
}
# This function takes the equivalent of a `bind_rows` for resample objects.
# Since bind rows does not call `useMethod` I had to call it something else
bind <- function(data) {
out <- list(data = data[[1]]$data, idx = unlist(map(data, pluck, "idx")))
class(out) <- "resample"
out
}
Then I just converted to a tibble in the same purrr closure in which my model is run for that CV. Problem solved! My memory usage is VERY low now.