I wrote the following code below that evaluates a function at every point contained in a very large "grid" (2981440 rows, 7 columns), and then summarizes these evaluations into a table. When the "grid" is small (e.g. 100 rows, 7 columns), the code runs successfully. But when the "grid" becomes large - I waited several hours, but the code still does not finish running.
Question: Using libraries such as "parallel", "foreach" and "SNOW" - is it possible to "give the computer an easier time with this code"? Perhaps using all the cores would result in this code running faster?
I am very new to parallel computing - could someone please take a look at this code below and let me know if you have any ideas on ways that I can make this code run faster?
library(dplyr)
library(data.table)
results_table <- data.frame()
grid_function <- function(train_data, random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate random quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
#create a table: for each bin, calculate the average of "diff"
final_table_2 = data.frame(final_table %>%
group_by(cat) %>%
summarize(
mean = mean(diff)
))
#add "total mean" to this table
final_table_2 = data.frame(final_table_2 %>% add_row(cat = "total", mean = mean(final_table$diff)))
#format this table: add the random criteria to this table for reference
final_table_2$random_1 = random_1
final_table_2$random_2 = random_2
final_table_2$random_3 = random_3
final_table_2$random_4 = random_4
final_table_2$split_1 = split_1
final_table_2$split_2 = split_2
final_table_2$split_3 = split_3
results_table <- rbind(results_table, final_table_2)
final_results = dcast(setDT(results_table), random_1 + random_2 + random_3 + random_4 + split_1 + split_2 + split_3 ~ cat, value.var = 'mean')
}
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
#grid
#grid_2
random_1 <- seq(80,100,5)
random_2 <- seq(85,120,5)
random_3 <- seq(85,120,5)
random_4 <- seq(90,120,5)
split_1 = seq(0,1,0.1)
split_2 = seq(0,1,0.1)
split_3 = seq(0,1,0.1)
DF_1 <- expand.grid(random_1 , random_2, random_3, random_4, split_1, split_2, split_3)
colnames(DF_1) <- c("random_1" , "random_2", "random_3", "random_4", "split_1", "split_2", "split_3")
train_data_new <- copy(train_data)
resultdf1 <- apply(DF_1,1, # 1 means rows
FUN=function(x){
do.call(
# Call Function grid_function2 with the arguments in
# a list
grid_function,
# force list type for the arguments
c(list(train_data_new), as.list(
# make the row to a named vector
unlist(x)
)
))
}
)
l = resultdf1
final_output = rbindlist(l, fill = TRUE)
Thanks