问题
I'm using R to do machine learning. Following standard machine learning methodology, I would like to randomly split my data into training, validation, and test data sets. How do I do that in R?
I know there are some related questions on how to split into 2 data sets (e.g. this post), but it is not obvious how to do it for 3 split data sets. By the way, the correct approach is to use 3 data sets (including a validation set to tune your hyperparameters).
回答1:
This linked approach for two groups (using floor) doesn't extend naturally to three. I'd do
spec = c(train = .6, test = .2, validate = .2)
g = sample(cut(
seq(nrow(df)),
nrow(df)*cumsum(c(0,spec)),
labels = names(spec)
))
res = split(df, g)
To check the results:
sapply(res, nrow)/nrow(df)
# train test validate
# 0.59375 0.18750 0.21875
# or...
addmargins(prop.table(table(g)))
# train test validate Sum
# 0.59375 0.18750 0.21875 1.00000
With set.seed(1) run just before, the result looks like
$train
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
$test
mpg cyl disp hp drat wt qsec vs am gear carb
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
$validate
mpg cyl disp hp drat wt qsec vs am gear carb
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Data.frames can be accessed like res$test or res[["test"]].
cut is the standard tool for partitioning based on shares.
回答2:
Following the approach shown in this post, here is working R code to divide a dataframe into three new dataframes for testing, validation, and test. The three subsets are non-overlapping.
# Create random training, validation, and test sets
# Set some input variables to define the splitting.
# Input 1. The data frame that you want to split into training, validation, and test.
df <- mtcars
# Input 2. Set the fractions of the dataframe you want to split into training,
# validation, and test.
fractionTraining <- 0.60
fractionValidation <- 0.20
fractionTest <- 0.20
# Compute sample sizes.
sampleSizeTraining <- floor(fractionTraining * nrow(df))
sampleSizeValidation <- floor(fractionValidation * nrow(df))
sampleSizeTest <- floor(fractionTest * nrow(df))
# Create the randomly-sampled indices for the dataframe. Use setdiff() to
# avoid overlapping subsets of indices.
indicesTraining <- sort(sample(seq_len(nrow(df)), size=sampleSizeTraining))
indicesNotTraining <- setdiff(seq_len(nrow(df)), indicesTraining)
indicesValidation <- sort(sample(indicesNotTraining, size=sampleSizeValidation))
indicesTest <- setdiff(indicesNotTraining, indicesValidation)
# Finally, output the three dataframes for training, validation and test.
dfTraining <- df[indicesTraining, ]
dfValidation <- df[indicesValidation, ]
dfTest <- df[indicesTest, ]
回答3:
Some of these seem overly complex, here's a simple way using sample to split any dataset into 3, or even an arbitrary number of sets.
# Simple into 3 sets.
idx <- sample(seq(1, 3), size = nrow(iris), replace = TRUE, prob = c(.8, .2, .2))
train <- iris[idx == 1,]
test <- iris[idx == 2,]
cal <- iris[idx == 3,]
If you'd rather reusable code:
# Or a function to split into arbitrary number of sets
test_split <- function(df, cuts, prob, ...)
{
idx <- sample(seq(1, cuts), size = nrow(df), replace = TRUE, prob = prob, ...)
z = list()
for (i in 1:cuts)
z[[i]] <- df[idx == i,]
z
}
z <- test_split(iris, 4, c(0.7, .1, .1, .1))
train <- z[1]
test <- z[2]
cal <- z[3]
other <- z[4]
回答4:
Here is one solution with a 60, 20 , 20 split that also ensures that there is no overlapping. However it is a trouble to adapt the split. If anyone could help me out, I appreciate it
# Draw a random, stratified sample including p percent of the data
idx.train <- createDataPartition(y = known$return_customer, p = 0.8, list = FALSE)
train <- known[idx.train, ] # training set with p = 0.8
# test set with p = 0.2 (drop all observations with train indeces)
test <- known[-idx.train, ]
idx.validation <- createDataPartition(y = train$return_customer, p = 0.25, list = FALSE) # Draw a random, stratified sample of ratio p of the data
validation <- train[idx.validation, ] #validation set with p = 0.8*0.25 = 0.2
train60 <- train[-idx.validation, ] #final train set with p= 0.8*0.75 = 0.6
回答5:
I think my approach is the easiest one:
idxTrain <- sample(nrow(dat),as.integer(nrow(dat)*0.7))
idxNotTrain <- which(! 1:nrow(dat) %in% idxTrain )
idxVal <- sample(idxNotTrain,as.integer(length(idxNotTrain)*0.333))
idxTest <- idxNotTrain[which(! idxNotTrain %in% idxVal)]
First, it splits the data into 70% training data and the rest (idxNotTrain). Then, the rest is again splitted into a validation data set (33%, 10% of the total data) and the rest (the testing data, 66%, 20% of the total data).
回答6:
Let me know if this would work. Just a simplified version
sample_train<- sample(seq_len(nrow(mtcars)), size = floor(0.60*nrow(mtcars)))
sample_valid<- sample(seq_len(nrow(mtcars)), size = floor(0.20*nrow(mtcars)))
sample_test <- sample(seq_len(nrow(mtcars)), size = floor(0.20*nrow(mtcars)))
train <- mtcars[sample_train, ]
validation<- mtcars[sample_valid, ]
test <- mtcars[sample_test, ]
来源：https://stackoverflow.com/questions/36068963/r-how-to-split-a-data-frame-into-training-validation-and-test-sets