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Re: st: Improving code speed
From
George Vega Yon <[email protected]>
To
[email protected]
Subject
Re: st: Improving code speed
Date
Thu, 23 May 2013 13:50:32 -0400
Dear Luis,
You can do both, translating part of your program into mata and
running it in parallel fashion. I recommend you to start by rewriting
the subloop, it could be something like this (is just an example),
following the previous example that I sent
______________ MATA ______________
cap mata: mata drop myfun()
mata:
real colvector myfun(real scalar reps, real colvector y, real colvector z) {
real scalar i, tsum
real colvector x
for(i=1;i<=reps;i++) {
x = z :+ rnormal(rows(z),1,0,1)
y = y :+ ((x):^2):/ sum(x)
}
return(y)
}
end
______________ MATA ______________
______________ STATA ______________
forvalues k=`=id[1]'(1)`=id[_N]'{
gen subs=(id<=`k') // Define the subsample to be used
gen Y`k'=0 // gen the intermediate Y`k'
mata: st_store(1::`k', "Y`k'", myfun($reps, st_data(1::`k', "Y`k'"),
st_data(1::`k',"z")))
replace Y`k'=Y`k'/$reps // average Y from the 100 simulations
replace Y= Y + Y`k'
drop Y`k' subs
}
______________ STATA ______________
Mata is fast, take this for example, two algorithms that generate the
same thing take quite different times to finish
____________________________
timer clear
// Stata loop + Substata loop
timer on 1
forval j = 1/1000 {
forval i = 1/1000 {
scalar x = sqrt(exp(1/c(pi))) + sqrt(exp(1/c(pi)))
}
}
timer off 1
// equals 2.3450392
di x
// Stata loop + mata loop
timer on 2
forval j = 1/1000 {
mata: for(i=1;i<=1000;i++) x = sqrt(exp(1/c("pi"))) + sqrt(exp(1/c("pi")))
}
timer off 2
// equals 2.3450392
mata:x
// In this example, the second version took ~ 1/12 of the time the first did
. timer list
1: 8.32 / 1 = 8.3240
2: 0.72 / 1 = 0.7220
____________________________
You will save a lot of time!
Best regards,
George Vega Yon
7 647 2552
http://cl.linkedin.com/in/georgevegayon
2013/5/23 Nick Cox <[email protected]>:
> Thanks for confirmation of mistakes.
>
> So, here is an extra comment as you are concerned about cleaning up
> your code and speeding it up. I can see no reason at all for copying
> variable -epsilon- to matrix -epsilon- using -mkmat-. You have a
> variable -epsilon- that should be fine for your purpose. In fact that
> was I suggested yesterday.
>
> If you post revised, complete and correct code you may get further
> comments, but no definite promises from me.
> Nick
> [email protected]
>
>
> On 23 May 2013 09:01, Luis Aguiar <[email protected]> wrote:
>> Hi George and Nick,
>>
>> Thanks a lot for your responses.
>>
>> Nick: Yes, the code works but there are indeed a couple of mistakes in
>> the one I copied. I actually use the command " mkmat epsilon if
>> id<=`reps' " after generating the variable epsilon (id is indeed _n in
>> a variable). The second line in your comment (b) should read " replace
>> x`i'=z + epsilon[`i',1] if id==`k' " . Sorry about all that. Thanks
>> for your helpful comments though, I will try to incorporate them into
>> my code along with George's comments in order to speed things up.
>>
>> George: Thanks for your suggestions as well. I wasn't sure if it would
>> be worth going into mata, but I will try it now. Your parallel code
>> seems very interesting too. Do you think it would go faster than using
>> mata?
>>
>> Again, thanks a lot to both of you!
>>
>> Cheers,
>> Luis
>>
>> 2013/5/22 Nick Cox <[email protected]>:
>>> Please use your full real name. See Statalist FAQ for that request and why.
>>>
>>> Some speed-ups are likely to be possible here, but I first I note
>>> several puzzles with this code.
>>>
>>> You don't say so, but presumably -id- is _n in a variable.
>>>
>>> -z- is unexplained.
>>>
>>> More problematically,
>>>
>>> (a) You -generate- a variable -epsilon- but you refer to a matrix -epsilon-.
>>>
>>> (b) The two lines below won't work as once -x`i'- exists the second
>>> command will fail.
>>>
>>> gen x`i'=z \\ generate simulated variable
>>> gen x`i'=z + epsilon[`i',1] if id==`k' \\ Add the random part
>>>
>>> Why do you say that it works? Did you copy some buggy version by accident?
>>>
>>> Better to post self-contained code that works.
>>>
>>> Speed-ups, apart from using Mata. (See George Vega Yon's post.)
>>>
>>> 1. Too much copying from one variable to another. I could be wrong,
>>> but some variables appear to be mostly zero, and you are just copying
>>> constants. Think in terms of scalars instead.
>>>
>>> 2. Use -summarize, meanonly- to get sums. -egen- is very slow at this.
>>>
>>> 3. Use -in 1/`k'- or -in `k'- wherever possible. Whenever there is a
>>> choice between -if- and -in- for the same problem, -in- is faster.
>>>
>>> Some example code:
>>>
>>> gen x`i'=z
>>> replace x`i'= x`i' + epsilon[`i'] in `k'
>>>
>>> su x`i' in 1/`k', meanonly
>>> replace Y`k' = (x`i')^2/r(sum) in `k'
>>>
>>>
>>> Nick
>>> [email protected]
>>>
>>>
>>> On 22 May 2013 18:25, Luis <[email protected]> wrote:
>>>> Dear statalist users,
>>>>
>>>> I am running into a "loop efficiency problem" in that I have to
>>>> construct a variable using many iterations and I am not sure whether I
>>>> am being as efficient as possible. Given the number of observations
>>>> that I have and with my current code, I have to wait days for my code
>>>> to finish running! Here's my problem:
>>>>
>>>> I have a total of 50000 observations and need to construct a variable
>>>> Y that will be computed using different subsamples of these
>>>> observations. In particular,
>>>> Y=Y1 when the subsample contains only the first observation,
>>>> Y=Y2 when the subsample contains observations 1 and 2,
>>>> Y=Y3 when the subsample contains observations 1, 2 and 3 etc until
>>>> Y=Y50000.
>>>>
>>>> The idea is therefore to loop over the sample and define the subsample
>>>> which contains observations 1 until k and construct the variable
>>>> Y`k'=Yk if id==k and Y`k'=0 if id!=k. Then sum the variables Y`k'
>>>> after each loop to end up with the final variable Y.
>>>>
>>>> To further complicate things, the variable Y needs to be the average
>>>> of 100 simulations that depend on draws taken from a normal
>>>> distribution. Hence I need to do a loop within the initial loop in
>>>> order to do the 100 simulations.
>>>>
>>>> My code therefore looks like this:
>>>>
>>>> _____________________________________________________________________________________
>>>>
>>>> gen Y=0
>>>>
>>>> local reps=100 \\ define the number of simulations
>>>>
>>>> gen epsilon=rnormal() \\ generate the random var for the simulations
>>>>
>>>> forvalues k=1(1)50000{
>>>>
>>>> gen subs=(id<=`k') \\ Define the subsample to be used
>>>> gen Y`k'=0 \\ gen the intermediate Y`k'
>>>>
>>>> forvalues i=1(1)`reps'{
>>>>
>>>> gen x`i'=z \\ generate simulated variable
>>>> gen x`i'=z + epsilon[`i',1] if id==`k' \\ Add the random part
>>>>
>>>> gen t=(x`i')^2
>>>> bysort subs: egen tsum=sum(x`i')
>>>>
>>>> gen Y_`i'=t/tsum if id ==`k' \\ Construct Y for simulation i
>>>> replace Y_`i'=0 if id!=`k'
>>>>
>>>> replace Y`k'=Y`k' + Y_`i'
>>>> replace Y`k'=0 if id!=`k'
>>>>
>>>> drop Y_`i' t tsum x`i'
>>>> }
>>>>
>>>> replace Y`k'=Y`k'/`reps' // average Y from the 100 simulations
>>>> replace Y= Y + Y`k'
>>>> drop Y`k' subs
>>>> }
>>>>
>>>> ____________________________________________________________________________________
>>>>
>>>>
>>>> The code runs fine, but I takes a lot of time since it has to
>>>> construct 100 variables for each of the 50000 iterations. I have tried
>>>> many different possibilities and I can't think of another way of
>>>> constructing Y.
>>>>
>>>> Any tip or suggestion that would help improve the efficiency of my
>>>> code would be greatly appreciated!!!
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