Jim, Dennis,
Once again, thanks for all your suggestions. After developing a more R-like
version of the script I terminated the running one after 976 (of 1697) reports
had been processed. At that point, the script had been running for approx.
33.5 hours! Here is the new version:
library(filehash)
db <- dbInit("/Volumes/Work on RDR Test Documents/R Databases/DB_TXT", type =
"RDS")
dbLoad(db)
dba <- dbInit("/Volumes/Work on RDR Test Documents/R Databases/DB_Aux", type =
"RDS")
dbLoad(dba)
tokens <- sentences.all.tokenized
stopwords <- stopwords.pubmed
# Convert to lowercase, remove beginning and end punctuation, tabulate
my.func <- function(sent, stop, ...){
list(
freq.table = (temp.table <- table(
sub(
"[[:punct:]]*$", "", sub(
"^[[:punct:]]*", "", tolower(sent)
)
)
)),
stopword.matches = (temp.matches <- match(names(temp.table), stop)),
stopword.summary = array(tapply(temp.table, !is.na(temp.matches), sum), dim
= 2, dimnames = list(c("no.non.stopwords", "no.stopwords")))
)
}
cat("Beginning at ", date(), ".\n", sep = "")
token.tables <-
lapply(1:length(tokens),
function(i.d, doc, stop, func, ...){
if ((i.d - 1) %% 10 == 0) cat((i.d - 1), " report(s) completed at ",
date(), ".\n", sep = "")
lapply(1:length(doc[[i.d]]),
function(i.s, sent, stop, func, ...){
func(sent[[i.s]], stop, ...)
}
, sent = doc[[i.d]], stop = stop, func = func, ...
)
}
,
doc = tokens, stop = stopwords, func = my.func
)
cat("Terminating at ", date(), ".\n", sep = "")
This script reaches the same point in approx. 1:09 hours, a little under 70
minutes!
What I am noticing now is a severe lack of real memory. Activity Monitor
shows about 20MB of real memory free. R, running in 64-bit mode, is using
6.75GB of real and 10GB of virtual memory. I see lots of disk activity. This
is undoubtedly the swapping between real and virtual memory. CPU activity is
very low. I suppose I could run the script twice, each time on half the
tokens. That would give me two lists, which I would have to combine into a
single one.
Regards,
Richard
On Thu, 12 Nov 2009 18:53:34 -0500, jim holtman wrote
Run the script on a small subset of the data and use Rprof to profile
the code. This will give you an idea of where time is being spent
and where to focus for improvement. I would suggest that you do not
convert the output of the 'table(t)' do a dataframe. You can just
extract the 'names' to get the words. You might be spending some of
the time in the accessing the information in the dataframe, which is
really not necessary for your code.
On Thu, Nov 12, 2009 at 2:12 AM, Richard R. Liu <richard.liu at pueo-
owl.ch> wrote:
I am running the following code on a MacBook Pro 17" Unibody early 2009 with
8GB RAM, OS X 10.5.8, R 2.10.0 Patch from Nov. 2, 2009, in 64-bit mode.
freq.stopwords <- numeric(0)
freq.nonstopwords <- numeric(0)
token.tables <- list(0)
i.ss <- c(0)
cat("Beginning at ", date(), ".\n")
for (i.d in 1:length(tokens)) {
? ? ? ?tt <- list(0)
? ? ? ?for (i.s in 1:length(tokens[[i.d]])) {
? ? ? ? ? ? ? ?t <- tolower(tokens[[i.d]][[i.s]])
? ? ? ? ? ? ? ?t <- sub("^[[:punct:]]*", "", t)
? ? ? ? ? ? ? ?t <- sub("[[:punct:]]*$", "", t)
? ? ? ? ? ? ? ?t <- as.data.frame(table(t))
? ? ? ? ? ? ? ?i.m <- match(t$t, stopwords)
? ? ? ? ? ? ? ?i.m.is.na <- is.na(i.m)
? ? ? ? ? ? ? ?i.ss <- i.ss + 1
? ? ? ? ? ? ? ?freq.stopwords[i.ss] <- sum(t$Freq * !i.m.is.na)
? ? ? ? ? ? ? ?freq.nonstopwords[i.ss] <- sum(t$Freq * i.m.is.na)
? ? ? ? ? ? ? ?tt[[i.s]] <- data.frame(token = t$t, freq = t$Freq,
matches.stopword = i.m)
? ? ? ?}
? ? ? ?token.tables[[i.d]] <- tt
? ? ? ?if (i.d %% 5 == 0) cat(i.d, "reports completed at ", date(), ".\n")
}
cat("Terminating at ", date(), ".\n")
The object in the innermost loop are:
* tokens: ?a list of lists. ?In the expression tokens[[i.d]][[i.s]], the
first index runs over 1697 reports, the second over the sentences in the
report, each of which consists of a vector of tokens, i.e., the character
strings between the white spaces in the sentence. ?One of the largest
reports takes up 58MB on the harddisk. ?Thus, the number of sentences can be
quite large, and some of the sentences are quite long (measure in tokens as
well as in characters).
* stopwords: ?is a vector of 571 words that occur very often in written
English.
The code operates on sentences, converting each token in the sentence to
lowercase, removing punctuation at the beginning and end of the token,
tabulating the frequency of the unique tokens, and generating an array that
indicates which tokens correspond to stopwords. ?It also sums the
frequencies of the stopwords and that of the non-stopwords. ?The result is a
list of list of dataframes.
I began running on Thursday Nov. 12, 2009 at 01:56:36. ?As of 7:52:00 510
reports had been processed. ?The Activity Monitor indicates no memory
bottleneck. ?R is using 4.31 GB of real memory, 7.23 GB of virtual memory,
and 1.67 GB of real memory are free.
I admit that I am an R newbie. ?From my understanding of the "apply"
functions (e.g., lapply), I see no way to use them to simplify the loops. ?I
would appreciate any suggestions about making the code more "R-like" and,
above all, much faster.
Regards,
Richard