[datatable-help] speeding up perception

Matthew,

I was hoping I misunderstood you first proposal, but I suspect I did not
;).

Personally, I find  DT[1,V1 <- 3] highly disturbing - I would expect it to
evaluate to
{ V1 <- 3; DT[1, V1] }
thus returning the first element of the third column.

Please see FAQ 1.1, since further below it seems to be an expectation
issue about 'with' syntax, too.

That said, I don't think it works, either. Taking you example and
data.table form r-forge:

[ snip ]

as you can see, DT is not modified.

Works for me on R 2.13.0. I'll try latest R later. If I can't reproduce
the non-working state I'll need some more environment information please.

sessionInfo()

sessionInfo()

DT = as.data.table(m)
for (i in 1:1000) DT[1,V1 <- 3]
DT[1,]

Also I suspect there is something quite amiss because even trivial things
don't work:

DF[1:4,1:4]

 V1 V2 V3 V4
1  3  1  1  1
2  1  1  1  1
3  1  1  1  1
4  1  1  1  1

DT[1:4,1:4]

[1] 1 2 3 4

That's correct and fundamental to data.table. See FAQs 1.1, 1.7, 1.8, 1.9
and 1.10.

When I first saw your proposal, I thought you have rather something like
within(DT, V1[1] <- 3)
in mind which looks innocent enough but performs terribly (note that I had
to scale down the loop by a factor of 100!!!):

system.time(for (i in 1:10) within(DT, V1[1] <- 3))

  user  system elapsed
 2.701   4.437   7.138

No, since 'with' is already built into data.table, I was thinking of
building 'within' in, too. I'll take a look at within(). Might as well
provide as many options as possible to the user to use as they wish.

With the for loop something like within(DF, for (i in 1:1000) V1[i] <- 3))
performs reasonably:

system.time(within(DT, for (i in 1:1000) V1[i] <- 3))

  user  system elapsed
 0.392   0.613   1.003

(Note: system.time() can be misleading when within() is involved, because
the expression is evaluated in a different environment so within() won't
actually change the object in the  global environment - it also interacts
with the possible duplication)

Noted, thanks. That's pretty fast. Does within() on data.frame fix the
original issue Ivo raised, then?  If so, job done.

Cheers,
Simon

On Jul 11, 2011, at 8:21 PM, Matthew Dowle wrote:

Thanks for the replies and info. An attempt at fast
assign is now committed to data.table v1.6.3 on
R-Forge. From NEWS :

o   Fast update is now implemented, FR#200.
  DT[i,j]<-value is now handled by data.table in C rather
  than falling through to data.frame methods.

  Thanks to Ivo Welch for raising speed issues on r-devel,
  to Simon Urbanek for the suggestion, and Luke Tierney and
  Simon for information on R internals.

  [<- syntax still incurs one working copy of the whole
  table (as of R 2.13.0) due to R's [<- dispatch mechanism
  copying to `*tmp*`, so, for ultimate speed and brevity,
  'within' syntax is now available as follows.

o   A new 'within' argument has been added to [.data.table,
  by default TRUE. It is very similar to the within()
  function in base R. If an assignment appears in j, it
  assigns to the column of DT, by reference; e.g.,

  DT[i,colname<-value]

  This syntax makes no copies of any part of memory at all.

m = matrix(1,nrow=100000,ncol=100)
DF = as.data.frame(m)
DT = as.data.table(m)
system.time(for (i in 1:1000) DF[1,1] <- 3)

     user  system elapsed
  287.730 323.196 613.453

system.time(for (i in 1:1000) DT[1,V1 <- 3])

     user  system elapsed
    1.152   0.004   1.161         # 528 times faster

Please note :

  *******************************************************
  **  Within syntax is presently highly experimental.  **
  *******************************************************

http://datatable.r-forge.r-project.org/


On Wed, 2011-07-06 at 09:08 -0500, luke-tierney at uiowa.edu wrote:

On Wed, 6 Jul 2011, Simon Urbanek wrote:

Interesting, and I stand corrected:

x = data.frame(a=1:n,b=1:n)
.Internal(inspect(x))

@103511c00 19 VECSXP g0c2 [OBJ,NAM(2),ATT] (len=2, tl=0)
@102c7b000 13 INTSXP g0c7 [] (len=100000, tl=0) 1,2,3,4,5,...
@102af3000 13 INTSXP g0c7 [] (len=100000, tl=0) 1,2,3,4,5,...

x[1,1]=42L
.Internal(inspect(x))

@10349c720 19 VECSXP g0c2 [OBJ,NAM(2),ATT] (len=2, tl=0)
@102c19000 13 INTSXP g0c7 [] (len=100000, tl=0) 42,2,3,4,5,...
@102b55000 13 INTSXP g0c7 [] (len=100000, tl=0) 1,2,3,4,5,...

x[[1]][1]=42L
.Internal(inspect(x))

@103511a78 19 VECSXP g1c2 [OBJ,MARK,NAM(2),ATT] (len=2, tl=0)
@102e65000 13 INTSXP g0c7 [] (len=100000, tl=0) 42,2,3,4,5,...
@101f14000 13 INTSXP g1c7 [MARK] (len=100000, tl=0) 1,2,3,4,5,...

x[[1]][1]=42L
.Internal(inspect(x))

@10349c800 19 VECSXP g0c2 [OBJ,NAM(2),ATT] (len=2, tl=0)
@102a2f000 13 INTSXP g0c7 [] (len=100000, tl=0) 42,2,3,4,5,...
@102ec7000 13 INTSXP g0c7 [] (len=100000, tl=0) 1,2,3,4,5,...


I have R to release ;) so I won't be looking into this right now, but
it's something worth investigating ... Since all the inner contents
have NAMED=0 I would not expect any duplication to be needed, but
apparently becomes so is at some point ...

The internals assume in various places that deep copies are made (one
of the reasons NAMED setings are not propagated to sub-sturcture).
The main issues are avoiding cycles and that there is no easy way to
check for sharing.  There may be some circumstances in which a shallow
copy would be OK but making sure it would be in all cases is probably
more trouble than it is worth at this point. (I've tried this in the
past in a few cases and always had to back off.)


Best,

luke

Cheers,
Simon


On Jul 6, 2011, at 4:36 AM, Matthew Dowle wrote:

On Tue, 2011-07-05 at 21:11 -0400, Simon Urbanek wrote:

No subassignment function satisfies that condition, because you can
always call them directly. However, that doesn't stop the default
method from making that assumption, so I'm not sure it's an issue.

David, Just to clarify - the data frame content is not copied, we
are talking about the vector holding columns.

If it is just the vector holding the columns that is copied (and not
the
columns themselves), why does n make a difference in this test (on R
2.13.0)?

n = 1000
x = data.frame(a=1:n,b=1:n)
system.time(for (i in 1:1000) x[1,1] <- 42L)

user  system elapsed
0.628   0.000   0.628

n = 100000
x = data.frame(a=1:n,b=1:n)      # still 2 columns, but longer
columns
system.time(for (i in 1:1000) x[1,1] <- 42L)

user  system elapsed
20.145   1.232  21.455

With $<- :

n = 1000
x = data.frame(a=1:n,b=1:n)
system.time(for (i in 1:1000) x$a[1] <- 42L)

user  system elapsed
0.304   0.000   0.307

n = 100000
x = data.frame(a=1:n,b=1:n)
system.time(for (i in 1:1000) x$a[1] <- 42L)

user  system elapsed
37.586   0.388  38.161

If it's because the 1st column needs to be copied (only) because
that's
the one being assigned to (in this test), that magnitude of slow down
doesn't seem consistent with the time of a vector copy of the 1st
column :

n=100000
v = 1:n
system.time(for (i in 1:1000) v[1] <- 42L)

user  system elapsed
0.016   0.000   0.017

system.time(for (i in 1:1000) {v2=v;v2[1] <- 42L})

user  system elapsed
1.816   1.076   2.900

Finally, increasing the number of columns, again only the 1st is
assigned to :

n=100000
x = data.frame(rep(list(1:n),100))
dim(x)

[1] 100000    100

system.time(for (i in 1:1000) x[1,1] <- 42L)

user  system elapsed
167.974  50.903 219.711

Cheers,
Simon

Sent from my iPhone

On Jul 5, 2011, at 9:01 PM, David Winsemius <dwinsemius at comcast.net>
wrote:

On Jul 5, 2011, at 7:18 PM, <luke-tierney at uiowa.edu>
<luke-tierney at uiowa.edu> wrote:

On Tue, 5 Jul 2011, Simon Urbanek wrote:

On Jul 5, 2011, at 2:08 PM, Matthew Dowle wrote:

Simon (and all),

I've tried to make assignment as fast as calling
`[<-.data.table`
directly, for user convenience. Profiling shows (IIUC) that it
isn't
dispatch, but x being copied. Is there a way to prevent '[<-'
from
copying x?

Good point, and conceptually, no. It's a subassignment after all
- see R-lang 3.4.4 - it is equivalent to

`*tmp*` <- x
x <- `[<-`(`*tmp*`, i, j, value)
rm(`*tmp*`)

so there is always a copy involved.

Now, a conceptual copy doesn't mean real copy in R since R tries
to keep the pass-by-value illusion while passing references in
cases where it knows that modifications cannot occur and/or they
are safe. The default subassign method uses that feature which
means it can afford to not duplicate if there is only one
reference -- then it's safe to not duplicate as we are replacing
that only existing reference. And in the case of a matrix, that
will be true at the latest from the second subassignment on.

Unfortunately the method dispatch (AFAICS) introduces one more
reference in the dispatch chain so there will always be two
references so duplication is necessary. Since we have only 0 / 1
/ 2+ information on the references, we can't distinguish whether
the second reference is due to the dispatch or due to the passed
object having more than one reference, so we have to duplicate in
any case. That is unfortunate, and I don't see a way around
(unless we handle subassignment methods is some special way).

I don't believe dispatch is bumping NAMED (and a quick experiment
seems to confirm this though I don't guarantee I did that right).
The
issue is that a replacement function implemented as a closure,
which
is the only option for a package, will always see NAMED on the
object
to be modified as 2 (because the value is obtained by forcing the
argument promise) and so any R level assignments will duplicate.
This
also isn't really an issue of imprecise reference counting --
there
really are (at least) two legitimate references -- one though the
argument and one through the caller's environment.

It would be good it we could come up with a way for packages to be
able to define replacement functions that do not duplicate in
cases
where we really don't want them to, but this would require coming
up
with some sort of protocol, minimally involving an efficient way
to
detect whether a replacement funciton is being called in a
replacement
context or directly.

Would "$<-" always satisfy that condition. It would be big help to
me if it could be designed to avoid duplication the rest of the
data.frame.

--

There are some replacement functions that use C code to cheat, but
these may create problems if called directly, so I won't advertise
them.

Best,

luke

Cheers,
Simon

--
Luke Tierney
Statistics and Actuarial Science
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa                  Phone:
319-335-3386
Department of Statistics and        Fax:
319-335-3017
Actuarial Science
241 Schaeffer Hall                  email:
luke at stat.uiowa.edu
Iowa City, IA 52242                 WWW:
http://www.stat.uiowa.edu______________________________________________
R-devel at r-project.org mailing list

David Winsemius, MD
West Hartford, CT

--
Luke Tierney
Statistics and Actuarial Science
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa                  Phone:             319-335-3386
Department of Statistics and        Fax:               319-335-3017
  Actuarial Science
241 Schaeffer Hall                  email:      luke at stat.uiowa.edu
Iowa City, IA 52242                 WWW:  http://www.stat.uiowa.edu