Utilizarea foreach

Programare paralelă în R

Nabeel Imam

Data Scientist

O buclă nouă

Bucla for nativă în R

numbers <- 1:1e6

sqroots <- rep(0, length(numbers))

for (i in 1:length(numbers)) {
  sqroots[i] <- sqrt(numbers[i])
}

Bucla foreach

numbers <- 1:1e6

library(foreach)


sqroots <- foreach(i = numbers) %do% {
  sqrt(i)
}

Bucle paralele

numbers <- 1:1e6

sqroots <- foreach(i = numbers) %do% {
  sqrt(i)
}

cl <- makeCluster(4)


library(doParallel)
registerDoParallel(cl)


sqroots <- foreach(i = numbers
                   # The parallel operator
                  ) %dopar% {
  sqrt(i)
}


stopCluster(cl)

Cele mai bune universități tehnice

print(uni_list)

 [1] "./uni_data/Argentina.csv"
 [2] "./uni_data/Australia.csv"
 [3] "./uni_data/Austria.csv"
 [4] "./uni_data/Azerbaijan.csv"
 [5] "./uni_data/Bahrain.csv"
 [6] "./uni_data/Bangladesh.csv"
 [7] "./uni_data/Belarus.csv"
 [8] "./uni_data/Belgium.csv"
 [9] "./uni_data/Bolivia.csv"
[10] "./uni_data/Bosnia and Herzegovina.csv"
...

cl <- makeCluster(4)

registerDoParallel(cl)


ls_df <- foreach(csv = uni_list) %dopar% {
  read.csv(csv)
}

stopCluster(cl)

Colectarea rezultatelor cu foreach

cl <- makeCluster(4)
registerDoParallel(cl)
ls_df <- foreach(csv = uni_list) %dopar% {
  read.csv(csv)
}
stopCluster(cl)

[[1]]
 location                  institution score
Argentina  Universidad de Buenos Aires  68.9
...
[[2]]
 location                     institution score
Australia  Australian National University  82.1
...

cl <- makeCluster(4)
registerDoParallel(cl)

df_uni <- foreach(csv = uni_list,
                 .combine = "rbind") %dopar% {
  read.csv(csv)
}
stopCluster(cl)

   location                     institution score
1 Argentina     Universidad de Buenos Aires  68.9
2 Argentina  Universidad Católica Argentina  33.3
3 Argentina     Universidad de Palermo (UP)  29.1
...

Citire, filtrare și combinare

library(dplyr)

n_unis <- 3


# Empty list
ls_df <- list()


for (i in 1:length(uni_list)) {
  # Read, filter, collect in empty list
  ls_df[[i]] <- read.csv(uni_list[[i]]) %>%
    top_n(n_unis, total_score)

}

# Combine the list into one
combined_df <- Reduce("rbind", ls_df)

foreach pentru victorie

n_unis <- 3


cl <- makeCluster(4)
registerDoParallel(cl)


df_top3 <- foreach(csv = uni_list,

                   .packages = "dplyr",

                   .export = "n_unis",

                   .combine = "rbind") %dopar% {

  read.csv(csv) %>% 
    top_n(n_unis, score)
}

stopCluster(cl)

 location                     institution score
Argentina     Universidad de Buenos Aires  68.9
Argentina  Universidad Católica Argentina  33.3
Argentina     Universidad de Palermo (UP)  29.1
Australia  Australian National University  82.1
Australia     The University of Melbourne  81.6
Australia        The University of Sydney  79.6
  Austria            University of Vienna  50.6
  Austria     Technische Universität Wien  45.7
...

Să exersăm!

Programare paralelă în R