How can I save randomly generated train and test datasets? - r

Question

I am using a for loop to generate 100 different train and test sets.
What I want to do now, is to save these 100 different train and test sets in order to be able to have a look at e.g. where iteration was 17.
This code shows my program with the for loop and the division into train and test set:
result_df<-matrix(ncol=3,nrow=100)
colnames(result_df)<-c("Acc","Sens","Spec")
for (g in 1:100 )
{
# Divide into Train and test set
smp_size <- floor(0.8 * nrow(mydata1))
train_ind <- sample(seq_len(nrow(mydata1)), size = smp_size)
train <- mydata1[train_ind, ]
test <- mydata1[-train_ind, ]
REST OF MY CODE
# Calculate some statistics
overall <- cm$overall
overall.accuracy <- format(overall['Accuracy'] * 100, nsmall =2, digits = 2)
overall.sensitivity <- format(cm$byClass['Sensitivity']* 100, nsmall =2, digits = 2)
overall.specificity <- format(cm$byClass['Specificity']* 100, nsmall =2, digits = 2)
result_df[g,1] <- overall.accuracy
result_df[g,2] <- overall.sensitivity
result_df[g,3] <- overall.specificity
}
How can I do this?

Answer 1

You could do the following, for example, saving each test and train sets as elements in a list:
result_df<-matrix(ncol=3,nrow=100)
colnames(result_df)<-c("Acc","Sens","Spec")
testlist <- list()
trainlist <- list()
for (g in 1:100 )
{
# Divide into Train and test set
smp_size <- floor(0.8 * nrow(mydata1))
train_ind <- sample(seq_len(nrow(mydata1)), size = smp_size)
train <- mydata1[train_ind, ]
test <- mydata1[-train_ind, ]
trainlist[[g]] <- train
testlist[[g]] <- test
}
EDIT
To retrieve the 7th element of these lists you could use trainlist[[7]]

Answer 2

You can save those in csv file by using the following method
write.csv(train, file = paste0("train-", Sys.time(), ".csv", sep=""))
write.csv(test, file = paste0("test-", Sys.time(), ".csv", sep=""))

Answer 3

One option could be to save the row indexes of your partitions, rather than saving all the datasets, and then select the rows indexes for the iteration you're interested in.
The caret package has a function called createDataPartition, which will do this for you:
library(caret)
df <- data.frame(col1 = rnorm(100), col2 = rnorm(100))
# create 100 partitions
train.idxs <- createDataPartition(1:nrow(df), times = 100, p = 0.8)
for(i in 1:length(train.idxs)) {
# create train and test sets
idx <- train.idxs[[i]]
train.df <- df[idx, ]
test.df <- df[-idx, ]
# calculate statistics ...
result_df[i,1] <- overall.accuracy
result_df[i,2] <- overall.sensitivity
result_df[i,3] <- overall.specificity
}
# check the datasets for the nth partition
# train set
df[train.idxs[[n]], ]
# test set
df[-train.idxs[[n]], ]

Answer 4

Put your code in a function and do a lapply():
result_df <- matrix(ncol=3, nrow=100)
colnames(result_df)<-c("Acc", "Sens", "Spec")
SIMg <- function(g) {
# Divide into Train and test set
smp_size <- floor(0.8 * nrow(mydata1))
train_ind <- sample(seq_len(nrow(mydata1)), size = smp_size)
train <- mydata1[train_ind, ]
test <- mydata1[-train_ind, ]
REST OF THE CODE
return(list(train=train, test=test, ...))
}
L <- lapply(1:100, SIMg)
The resulting list L has 100 elements, each element is a list containing the two dataframes and your results for one simulation run.
To get separate lists trainlist and testlist you can do:
trainlist <- lallpy(L, '[[', "train")
testlist <- lallpy(L, '[[', "test")