On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net>
wrote:
(Sorry to be a bit slow responding.)
You have not supplied a complete example, which would be good in this
case because what you are suggesting could be a serious bug in R or a
package. Serious journals require reproducibility these days. For
example, JSS is very clear on this point.
To your question
> My question simply is: should the location of the set.seed command
matter,
> provided that it is applied before any commands which involve randomness
> (such as partitioning)?
the answer is no, it should not matter. But the proviso is important.
You can determine where things are messing up using something like
set.seed(654321)
zk <- RNGkind() # [1] "Mersenne-Twister"
"Inversion"
zk
z <- runif(2)
z
set.seed(654321)
# install.packages(c('caret', 'ggplot2', 'e1071'))
library(caret)
all(runif(2) == z) # should be true but it is not always
set.seed(654321)
library(ggplot2)
all(runif(2) == z) # should be true
set.seed(654321)
library(e1071)
all(runif(2) == z) # should be true
all(RNGkind() == zk) # should be true
On my computer package caret seems to sometimes, but not always, do
something that advances or changes the RNG. So you will need to set the
seed after that package is loaded if you want reproducibility.
As Bill Dunlap points out, parallel can introduce much more complicated
issues. If you are in fact using parallel then we really need a new
thread with a better subject line, and the discussion will get much messier.
The short answer is that, yes you should be able to get reproducible
results with parallel computing. If you cannot then you are almost
certainly doing something wrong. To publish you really must have
reproducible results.
In the example that Bill gave, I think the problem is that set.seed()
only resets the seed in the main thread, the nodes continue to operate
with unreset RNG. To demonstrate this to yourself you can do
library(parallel)
cl <- parallel::makeCluster(3)
parallel::clusterCall(cl, function()set.seed(100))
parallel::clusterCall(cl, function()RNGkind())
parallel::clusterCall(cl, function()runif(2)) # similar result from
all nodes
# [1] 0.3077661 0.2576725
However, do *NOT* do that in real work. You will be getting the same RNG
stream from each node. If you are using random numbers and parallel you
need to read a lot more, and probably consider a variant of the
"L'Ecuyer" generator or something designed for parallel computing.
One special point I will mention because it does not seem to be widely
appreciated: the number of nodes affects the random stream, so recording
the number of compute nodes along with the RNG and seed information is
important for reproducible results. This has the unfortunate consequence
that an experiment cannot be reproduced on a larger cluster. (If anyone
knows differently I would very much like to hear.)
Paul Gilbert
> Hi all,
>
> For some odd reason when running na?ve bayes, k-NN, etc., I get slightly
> different results (e.g., error rates, classification probabilities) from
> run
> to run even though I am using the same random seed.
>
> Nothing else (input-wise) is changing, but my results are somewhat
> different
> from run to run. The only randomness should be in the partitioning,
and I
> have set the seed before this point.
>
> My question simply is: should the location of the set.seed command
matter,
> provided that it is applied before any commands which involve randomness
> (such as partitioning)?
>
> If you need to see the code, it is below:
>
> Thank you,
> Gary
>
>
> A. Separate the original (in-sample) data from the new
(out-of-sample)
> data. Set a random seed.
>
>> InvestTech <- as.data.frame(InvestTechRevised)
>> outOfSample <- InvestTech[5001:nrow(InvestTech), ]
>> InvestTech <- InvestTech[1:5000, ]
>> set.seed(654321)
>
> B. Install and load the caret, ggplot2 and e1071 packages.
>
>> install.packages(?caret?)
>> install.packages(?ggplot2?)
>> install.packages(?e1071?)
>> library(caret)
>> library(ggplot2)
>> library(e1071)
>
> C. Bin the predictor variables with approximately equal counts using
> the cut_number function from the ggplot2 package. We will use 20 bins.
>
>> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
>> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
>> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20)
>> outOfSample[, 2] <- cut_number(outOfSample[, 2], n = 20)
>
> D. Partition the original (in-sample) data into 60% training and 40%
> validation sets.
>
>> n <- nrow(InvestTech)
>> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
>> InvestTechTrain <- InvestTech[train, ]
>> InvestTechVal <- InvestTech[-train, ]
>
> E. Use the naiveBayes function in the e1071 package to fit the
model.
>
>> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data =
InvestTechTrain)
>> prob <- predict(model, newdata = InvestTechVal, type = ?raw?)
>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
>
> F. Use the confusionMatrix function in the caret package to
output the
> confusion matrix.
>
>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode
> ?everything?, positive = ?1?)
>> accuracy <- confMtr$overall[1]
>> valError <- 1 ? accuracy
>> confMtr
>
> G. Classify the 18 new (out-of-sample) readers using the following
> code.
>> prob <- predict(model, newdata = outOfSample, type = ?raw?)
>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
>> cbind(pred, prob, outOfSample[, -3])
>
>
>
If your computations involve the parallel package then set.seed(seed)
may not produce repeatable results. E.g.,
> cl <- parallel::makeCluster(3) # Create cluster with 3 nodes on local
host
> set.seed(100); runif(2)
[1] 0.3077661 0.2576725
> parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1))
[,1] [,2] [,3]
[1,] 101.7779 102.5308 103.3459
[2,] 101.8128 102.6114 103.9102
>
> set.seed(100); runif(2)
[1] 0.3077661 0.2576725
> parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1))
[,1] [,2] [,3]
[1,] 101.1628 102.9643 103.2684
[2,] 101.9205 102.6937 103.7907
Bill Dunlap
TIBCO Software
wdunlap tibco.com
Thank you, everybody, who replied! I appreciate your valuable advise! I will move the location of the set.seed() command to after all packages have been installed and loaded. Best regards, Gary Sent from my iPad> On Mar 4, 2018, at 12:18 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: > > On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> > wrote: > > (Sorry to be a bit slow responding.) > > You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. > > To your question > > My question simply is: should the location of the set.seed command matter, > > provided that it is applied before any commands which involve randomness > > (such as partitioning)? > > the answer is no, it should not matter. But the proviso is important. > > You can determine where things are messing up using something like > > set.seed(654321) > zk <- RNGkind() # [1] "Mersenne-Twister" "Inversion" > zk > z <- runif(2) > z > set.seed(654321) > > # install.packages(c('caret', 'ggplot2', 'e1071')) > library(caret) > all(runif(2) == z) # should be true but it is not always > > set.seed(654321) > library(ggplot2) > all(runif(2) == z) # should be true > > set.seed(654321) > library(e1071) > all(runif(2) == z) # should be true > > all(RNGkind() == zk) # should be true > > On my computer package caret seems to sometimes, but not always, do something that advances or changes the RNG. So you will need to set the seed after that package is loaded if you want reproducibility. > > As Bill Dunlap points out, parallel can introduce much more complicated issues. If you are in fact using parallel then we really need a new thread with a better subject line, and the discussion will get much messier. > > The short answer is that, yes you should be able to get reproducible results with parallel computing. If you cannot then you are almost certainly doing something wrong. To publish you really must have reproducible results. > > In the example that Bill gave, I think the problem is that set.seed() only resets the seed in the main thread, the nodes continue to operate with unreset RNG. To demonstrate this to yourself you can do > > library(parallel) > cl <- parallel::makeCluster(3) > parallel::clusterCall(cl, function()set.seed(100)) > parallel::clusterCall(cl, function()RNGkind()) > parallel::clusterCall(cl, function()runif(2)) # similar result from all nodes > # [1] 0.3077661 0.2576725 > > However, do *NOT* do that in real work. You will be getting the same RNG stream from each node. If you are using random numbers and parallel you need to read a lot more, and probably consider a variant of the "L'Ecuyer" generator or something designed for parallel computing. > > One special point I will mention because it does not seem to be widely > appreciated: the number of nodes affects the random stream, so recording the number of compute nodes along with the RNG and seed information is important for reproducible results. This has the unfortunate consequence that an experiment cannot be reproduced on a larger cluster. (If anyone knows differently I would very much like to hear.) > > Paul Gilbert > > > > Hi all, > > > > For some odd reason when running na?ve bayes, k-NN, etc., I get slightly > > different results (e.g., error rates, classification probabilities) from > > run > > to run even though I am using the same random seed. > > > > Nothing else (input-wise) is changing, but my results are somewhat > > different > > from run to run. The only randomness should be in the partitioning, and I > > have set the seed before this point. > > > > My question simply is: should the location of the set.seed command matter, > > provided that it is applied before any commands which involve randomness > > (such as partitioning)? > > > > If you need to see the code, it is below: > > > > Thank you, > > Gary > > > > > > A. Separate the original (in-sample) data from the new (out-of-sample) > > data. Set a random seed. > > > >> InvestTech <- as.data.frame(InvestTechRevised) > >> outOfSample <- InvestTech[5001:nrow(InvestTech), ] > >> InvestTech <- InvestTech[1:5000, ] > >> set.seed(654321) > > > > B. Install and load the caret, ggplot2 and e1071 packages. > > > >> install.packages(?caret?) > >> install.packages(?ggplot2?) > >> install.packages(?e1071?) > >> library(caret) > >> library(ggplot2) > >> library(e1071) > > > > C. Bin the predictor variables with approximately equal counts using > > the cut_number function from the ggplot2 package. We will use 20 bins. > > > >> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20) > >> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20) > >> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20) > >> outOfSample[, 2] <- cut_number(outOfSample[, 2], n = 20) > > > > D. Partition the original (in-sample) data into 60% training and 40% > > validation sets. > > > >> n <- nrow(InvestTech) > >> train <- sample(1:n, size = 0.6 * n, replace = FALSE) > >> InvestTechTrain <- InvestTech[train, ] > >> InvestTechVal <- InvestTech[-train, ] > > > > E. Use the naiveBayes function in the e1071 package to fit the model. > > > >> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) > >> prob <- predict(model, newdata = InvestTechVal, type = ?raw?) > >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) > > > > F. Use the confusionMatrix function in the caret package to output the > > confusion matrix. > > > >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode > > ?everything?, positive = ?1?) > >> accuracy <- confMtr$overall[1] > >> valError <- 1 ? accuracy > >> confMtr > > > > G. Classify the 18 new (out-of-sample) readers using the following > > code. > >> prob <- predict(model, newdata = outOfSample, type = ?raw?) > >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) > >> cbind(pred, prob, outOfSample[, -3]) > > > > > > > > > If your computations involve the parallel package then set.seed(seed) > may not produce repeatable results. E.g., > > > cl <- parallel::makeCluster(3) # Create cluster with 3 nodes on local > host > > set.seed(100); runif(2) > [1] 0.3077661 0.2576725 > > parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1)) > [,1] [,2] [,3] > [1,] 101.7779 102.5308 103.3459 > [2,] 101.8128 102.6114 103.9102 > > > > set.seed(100); runif(2) > [1] 0.3077661 0.2576725 > > parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1)) > [,1] [,2] [,3] > [1,] 101.1628 102.9643 103.2684 > [2,] 101.9205 102.6937 103.7907 > > > Bill Dunlap > TIBCO Software > wdunlap tibco.com
The following helps identify when .GlobalEnv$.Random.seed has changed:
rng_tracker <- local({
last <- .GlobalEnv$.Random.seed
function(...) {
curr <- .GlobalEnv$.Random.seed
if (!identical(curr, last)) {
warning(".Random.seed changed")
last <<- curr
}
TRUE
}
})
addTaskCallback(rng_tracker, name = "RNG tracker")
EXAMPLE:
> sample.int(1L)
[1] 1
Warning: .Random.seed changed
This will help you find for instance:
## Loading ggplot2 does not affect the RNG seed> loadNamespace("ggplot2")
<environment: namespace:ggplot2>
## But attaching it does> library("ggplot2")
Warning: .Random.seed changed
which reveals:
> ggplot2:::.onAttach
function (...)
{
if (!interactive() || stats::runif(1) > 0.1)
return()
tips <- c("Need help? Try the ggplot2 mailing list:
http://groups.google.com/group/ggplot2.",
"Find out what's changed in ggplot2 at
http://github.com/tidyverse/ggplot2/releases.",
"Use suppressPackageStartupMessages() to eliminate package
startup messages.",
"Stackoverflow is a great place to get help:
http://stackoverflow.com/tags/ggplot2.",
"Need help getting started? Try the cookbook for R:
http://www.cookbook-r.com/Graphs/",
"Want to understand how all the pieces fit together? Buy the
ggplot2 book: http://ggplot2.org/book/")
tip <- sample(tips, 1)
packageStartupMessage(paste(strwrap(tip), collapse = "\n"))
}
<environment: namespace:ggplot2>
There are probably many case of this in different R packages.
R WISH:
There could be a
preserveRandomSeed({
tip <- sample(tips, 1)
})
function in R for these type of random needs where true random
properties are non-critical. This type of
"draw-a-random-number-and-reset-the-seed" is for instance used in
parallel:::initDefaultClusterOptions() which is called when the
'parallel' package is loaded:
seed <- .GlobalEnv$.Random.seed
ran1 <- sample.int(.Machine$integer.max - 1L, 1L) / .Machine$integer.max
port <- 11000 + 1000 * ((ran1 + unclass(Sys.time()) / 300) %% 1)
if(is.null(seed)) ## there was none, initially
rm( ".Random.seed", envir = .GlobalEnv, inherits = FALSE)
else # reset
assign(".Random.seed", seed, envir = .GlobalEnv, inherits = FALSE)
/Henrik
On Sun, Mar 4, 2018 at 1:40 PM, Gary Black <gwblack001 at sbcglobal.net>
wrote:> Thank you, everybody, who replied! I appreciate your valuable advise! I
will move the location of the set.seed() command to after all packages have been
installed and loaded.
>
> Best regards,
> Gary
>
> Sent from my iPad
>
>> On Mar 4, 2018, at 12:18 PM, Paul Gilbert <pgilbert902 at
gmail.com> wrote:
>>
>> On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at
sbcglobal.net>
>> wrote:
>>
>> (Sorry to be a bit slow responding.)
>>
>> You have not supplied a complete example, which would be good in this
case because what you are suggesting could be a serious bug in R or a package.
Serious journals require reproducibility these days. For example, JSS is very
clear on this point.
>>
>> To your question
>> > My question simply is: should the location of the set.seed
command matter,
>> > provided that it is applied before any commands which involve
randomness
>> > (such as partitioning)?
>>
>> the answer is no, it should not matter. But the proviso is important.
>>
>> You can determine where things are messing up using something like
>>
>> set.seed(654321)
>> zk <- RNGkind() # [1] "Mersenne-Twister"
"Inversion"
>> zk
>> z <- runif(2)
>> z
>> set.seed(654321)
>>
>> # install.packages(c('caret', 'ggplot2',
'e1071'))
>> library(caret)
>> all(runif(2) == z) # should be true but it is not always
>>
>> set.seed(654321)
>> library(ggplot2)
>> all(runif(2) == z) # should be true
>>
>> set.seed(654321)
>> library(e1071)
>> all(runif(2) == z) # should be true
>>
>> all(RNGkind() == zk) # should be true
>>
>> On my computer package caret seems to sometimes, but not always, do
something that advances or changes the RNG. So you will need to set the seed
after that package is loaded if you want reproducibility.
>>
>> As Bill Dunlap points out, parallel can introduce much more complicated
issues. If you are in fact using parallel then we really need a new thread with
a better subject line, and the discussion will get much messier.
>>
>> The short answer is that, yes you should be able to get reproducible
results with parallel computing. If you cannot then you are almost certainly
doing something wrong. To publish you really must have reproducible results.
>>
>> In the example that Bill gave, I think the problem is that set.seed()
only resets the seed in the main thread, the nodes continue to operate with
unreset RNG. To demonstrate this to yourself you can do
>>
>> library(parallel)
>> cl <- parallel::makeCluster(3)
>> parallel::clusterCall(cl, function()set.seed(100))
>> parallel::clusterCall(cl, function()RNGkind())
>> parallel::clusterCall(cl, function()runif(2)) # similar result from all
nodes
>> # [1] 0.3077661 0.2576725
>>
>> However, do *NOT* do that in real work. You will be getting the same
RNG stream from each node. If you are using random numbers and parallel you need
to read a lot more, and probably consider a variant of the
"L'Ecuyer" generator or something designed for parallel computing.
>>
>> One special point I will mention because it does not seem to be widely
>> appreciated: the number of nodes affects the random stream, so
recording the number of compute nodes along with the RNG and seed information is
important for reproducible results. This has the unfortunate consequence that an
experiment cannot be reproduced on a larger cluster. (If anyone knows
differently I would very much like to hear.)
>>
>> Paul Gilbert
>>
>>
>> > Hi all,
>> >
>> > For some odd reason when running na?ve bayes, k-NN, etc., I get
slightly
>> > different results (e.g., error rates, classification
probabilities) from
>> > run
>> > to run even though I am using the same random seed.
>> >
>> > Nothing else (input-wise) is changing, but my results are somewhat
>> > different
>> > from run to run. The only randomness should be in the
partitioning, and I
>> > have set the seed before this point.
>> >
>> > My question simply is: should the location of the set.seed
command matter,
>> > provided that it is applied before any commands which involve
randomness
>> > (such as partitioning)?
>> >
>> > If you need to see the code, it is below:
>> >
>> > Thank you,
>> > Gary
>> >
>> >
>> > A. Separate the original (in-sample) data from the new
(out-of-sample)
>> > data. Set a random seed.
>> >
>> >> InvestTech <- as.data.frame(InvestTechRevised)
>> >> outOfSample <- InvestTech[5001:nrow(InvestTech), ]
>> >> InvestTech <- InvestTech[1:5000, ]
>> >> set.seed(654321)
>> >
>> > B. Install and load the caret, ggplot2 and e1071 packages.
>> >
>> >> install.packages(?caret?)
>> >> install.packages(?ggplot2?)
>> >> install.packages(?e1071?)
>> >> library(caret)
>> >> library(ggplot2)
>> >> library(e1071)
>> >
>> > C. Bin the predictor variables with approximately equal
counts using
>> > the cut_number function from the ggplot2 package. We will use 20
bins.
>> >
>> >> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
>> >> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
>> >> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20)
>> >> outOfSample[, 2] <- cut_number(outOfSample[, 2], n = 20)
>> >
>> > D. Partition the original (in-sample) data into 60% training
and 40%
>> > validation sets.
>> >
>> >> n <- nrow(InvestTech)
>> >> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
>> >> InvestTechTrain <- InvestTech[train, ]
>> >> InvestTechVal <- InvestTech[-train, ]
>> >
>> > E. Use the naiveBayes function in the e1071 package to fit
the model.
>> >
>> >> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data =
InvestTechTrain)
>> >> prob <- predict(model, newdata = InvestTechVal, type =
?raw?)
>> >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
>> >
>> > F. Use the confusionMatrix function in the caret package to
output the
>> > confusion matrix.
>> >
>> >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[,
3]),mode >> > ?everything?, positive = ?1?)
>> >> accuracy <- confMtr$overall[1]
>> >> valError <- 1 ? accuracy
>> >> confMtr
>> >
>> > G. Classify the 18 new (out-of-sample) readers using the
following
>> > code.
>> >> prob <- predict(model, newdata = outOfSample, type = ?raw?)
>> >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
>> >> cbind(pred, prob, outOfSample[, -3])
>> >
>> >
>> >
>>
>>
>> If your computations involve the parallel package then set.seed(seed)
>> may not produce repeatable results. E.g.,
>>
>> > cl <- parallel::makeCluster(3) # Create cluster with 3 nodes
on local
>> host
>> > set.seed(100); runif(2)
>> [1] 0.3077661 0.2576725
>> > parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1))
>> [,1] [,2] [,3]
>> [1,] 101.7779 102.5308 103.3459
>> [2,] 101.8128 102.6114 103.9102
>> >
>> > set.seed(100); runif(2)
>> [1] 0.3077661 0.2576725
>> > parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1))
>> [,1] [,2] [,3]
>> [1,] 101.1628 102.9643 103.2684
>> [2,] 101.9205 102.6937 103.7907
>>
>>
>> Bill Dunlap
>> TIBCO Software
>> wdunlap tibco.com
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
On Sun, Mar 4, 2018 at 10:18 AM, Paul Gilbert <pgilbert902 at gmail.com> wrote:> On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> > wrote: > > (Sorry to be a bit slow responding.) > > You have not supplied a complete example, which would be good in this case > because what you are suggesting could be a serious bug in R or a package. > Serious journals require reproducibility these days. For example, JSS is > very clear on this point. > > To your question >> My question simply is: should the location of the set.seed command >> matter, >> provided that it is applied before any commands which involve randomness >> (such as partitioning)? > > the answer is no, it should not matter. But the proviso is important. > > You can determine where things are messing up using something like > > set.seed(654321) > zk <- RNGkind() # [1] "Mersenne-Twister" "Inversion" > zk > z <- runif(2) > z > set.seed(654321) > > # install.packages(c('caret', 'ggplot2', 'e1071')) > library(caret) > all(runif(2) == z) # should be true but it is not always > > set.seed(654321) > library(ggplot2) > all(runif(2) == z) # should be true > > set.seed(654321) > library(e1071) > all(runif(2) == z) # should be true > > all(RNGkind() == zk) # should be true > > On my computer package caret seems to sometimes, but not always, do > something that advances or changes the RNG. So you will need to set the seed > after that package is loaded if you want reproducibility. > > As Bill Dunlap points out, parallel can introduce much more complicated > issues. If you are in fact using parallel then we really need a new thread > with a better subject line, and the discussion will get much messier. > > The short answer is that, yes you should be able to get reproducible results > with parallel computing. If you cannot then you are almost certainly doing > something wrong. To publish you really must have reproducible results. > > In the example that Bill gave, I think the problem is that set.seed() only > resets the seed in the main thread, the nodes continue to operate with > unreset RNG. To demonstrate this to yourself you can do > > library(parallel) > cl <- parallel::makeCluster(3) > parallel::clusterCall(cl, function()set.seed(100)) > parallel::clusterCall(cl, function()RNGkind()) > parallel::clusterCall(cl, function()runif(2)) # similar result from all > nodes > # [1] 0.3077661 0.2576725 > > However, do *NOT* do that in real work. You will be getting the same RNG > stream from each node. If you are using random numbers and parallel you need > to read a lot more, and probably consider a variant of the "L'Ecuyer" > generator or something designed for parallel computing. > > One special point I will mention because it does not seem to be widely > appreciated: the number of nodes affects the random stream, so recording the > number of compute nodes along with the RNG and seed information is important > for reproducible results. This has the unfortunate consequence that an > experiment cannot be reproduced on a larger cluster. (If anyone knows > differently I would very much like to hear.)[Disclaimer: I'm the author] future.apply::future_lapply(X, ..., future.seed) etc. can produce identical RNG results regardless of how 'X' is chunked up. For example, library(future.apply) task <- function(i) { c(i = i, random = sample.int(10, size = 1), pid = Sys.getpid()) } y <- list() plan(multiprocess, workers = 1L) y[[1]] <- future_sapply(1:10, FUN = task, future.seed = 42) plan(multiprocess, workers = 2L) y[[2]] <- future_sapply(1:10, FUN = task, future.seed = 42) plan(multiprocess, workers = 3L) y[[3]] <- future_sapply(1:10, FUN = task, future.seed = 42) gives the exact same random output:> y[[1]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] i 1 2 3 4 5 6 7 8 9 10 random 5 10 1 8 7 9 3 5 10 4 pid 31933 31933 31933 31933 31933 31933 31933 31933 31933 31933 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] i 1 2 3 4 5 6 7 8 9 10 random 5 10 1 8 7 9 3 5 10 4 pid 32141 32141 32141 32141 32141 32142 32142 32142 32142 32142 [[3]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] i 1 2 3 4 5 6 7 8 9 10 random 5 10 1 8 7 9 3 5 10 4 pid 32199 32199 32199 32200 32200 32200 32200 32201 32201 32201 To base the RNG on the current RNG seed (== .GlobalEnv$.Random.seed), one can use 'future.seed = TRUE'. For performance reasons, I choose the default to be 'future.seed = FALSE', because there can be a substantial overhead in setting up reproducible L'Ecuyer subRNG-streams for all elements in 'X'. I think the snowFT package by Sevcikova & Rossini also provides this mechanism; Hana Sevcikova is also behind the rlecuyer package. Hope this helps /Henrik> > Paul Gilbert > > > >> Hi all, >> >> For some odd reason when running na?ve bayes, k-NN, etc., I get slightly >> different results (e.g., error rates, classification probabilities) from >> run >> to run even though I am using the same random seed. >> >> Nothing else (input-wise) is changing, but my results are somewhat >> different >> from run to run. The only randomness should be in the partitioning, and I >> have set the seed before this point. >> >> My question simply is: should the location of the set.seed command >> matter, >> provided that it is applied before any commands which involve randomness >> (such as partitioning)? >> >> If you need to see the code, it is below: >> >> Thank you, >> Gary >> >> >> A. Separate the original (in-sample) data from the new >> (out-of-sample) >> data. Set a random seed. >> >>> InvestTech <- as.data.frame(InvestTechRevised) >>> outOfSample <- InvestTech[5001:nrow(InvestTech), ] >>> InvestTech <- InvestTech[1:5000, ] >>> set.seed(654321) >> >> B. Install and load the caret, ggplot2 and e1071 packages. >> >>> install.packages(?caret?) >>> install.packages(?ggplot2?) >>> install.packages(?e1071?) >>> library(caret) >>> library(ggplot2) >>> library(e1071) >> >> C. Bin the predictor variables with approximately equal counts using >> the cut_number function from the ggplot2 package. We will use 20 bins. >> >>> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20) >>> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20) >>> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20) >>> outOfSample[, 2] <- cut_number(outOfSample[, 2], n = 20) >> >> D. Partition the original (in-sample) data into 60% training and 40% >> validation sets. >> >>> n <- nrow(InvestTech) >>> train <- sample(1:n, size = 0.6 * n, replace = FALSE) >>> InvestTechTrain <- InvestTech[train, ] >>> InvestTechVal <- InvestTech[-train, ] >> >> E. Use the naiveBayes function in the e1071 package to fit the model. >> >>> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) >>> prob <- predict(model, newdata = InvestTechVal, type = ?raw?) >>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) >> >> F. Use the confusionMatrix function in the caret package to output >> the >> confusion matrix. >> >>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode >> ?everything?, positive = ?1?) >>> accuracy <- confMtr$overall[1] >>> valError <- 1 ? accuracy >>> confMtr >> >> G. Classify the 18 new (out-of-sample) readers using the following >> code. >>> prob <- predict(model, newdata = outOfSample, type = ?raw?) >>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) >>> cbind(pred, prob, outOfSample[, -3]) >> >> >> > > > If your computations involve the parallel package then set.seed(seed) > may not produce repeatable results. E.g., > >> cl <- parallel::makeCluster(3) # Create cluster with 3 nodes on local > host >> set.seed(100); runif(2) > [1] 0.3077661 0.2576725 >> parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1)) > [,1] [,2] [,3] > [1,] 101.7779 102.5308 103.3459 > [2,] 101.8128 102.6114 103.9102 >> >> set.seed(100); runif(2) > [1] 0.3077661 0.2576725 >> parallel::parSapply(cl, 101:103, function(i)runif(2, i, i+1)) > [,1] [,2] [,3] > [1,] 101.1628 102.9643 103.2684 > [2,] 101.9205 102.6937 103.7907 > > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.