On 5/6/2010 1:51 PM, Dave Angel wrote:
Art Kendall wrote:
On 5/6/2010 11:14 AM, Dave Angel wrote:
Art Kendall wrote:
I am running Windows 7 64bit Home premium. with quad cpus and 8G
memory. I am using Python 2.6.2.
I have all the Federalist Papers concatenated into one .txt file.
Which is how big? Currently you (unnecessarily) load the entire
thing into memory with readlines(). And then you do confusing work
to split it apart again, into one list element per paper. And for
a while there, you have three copies of the entire text. You're
keeping two copies, in the form of alltext and papers.
You print out the len(papers). What do you see there? Is it
correctly 87 ? If it's not, you have to fix the problem here,
before even going on.
I want to prepare a file with a row for each paper and a column
for each term. The cells would contain the count of a term in that
paper. In the original application in the 1950's 30 single word
terms were used. I can now use NoteTab to get a list of all the
8708 separate words in allWords.txt. I can then use that data in
statistical exploration of the set of texts.
I have the python program(?) syntax(?) script(?) below that I am
using to learn PYTHON. The comments starting with "later" are
things I will try to do to make this more useful. I am getting one
step at at time to work
It works when the number of terms in the term list is small e.g.,
10. I get a file with the correct number of rows (87) and count
columns (10) in termcounts.txt. The termcounts.txt file is not
correct when I have a larger number of terms, e.g., 100. I get a
file with only 40 rows and the correct number of columns. With
8700 terms I get only 40 rows I need to be able to have about 8700
terms. (If this were FORTRAN I would say that the subscript indices
were getting scrambled.) (As I develop this I would like to be
open-ended with the numbers of input papers and open ended with the
number of words/terms.)
# word counts: Federalist papers
import re, textwrap
# read the combined file and split into individual papers
# later create a new version that deals with all files in a folder
rather than having papers concatenated
alltext = file("C:/Users/Art/Desktop/fed/feder16v3.txt").readlines()
papers= re.split(r'FEDERALIST No\.'," ".join(alltext))
print len(papers)
countsfile = file("C:/Users/Art/desktop/fed/TermCounts.txt", "w")
syntaxfile = file("C:/Users/Art/desktop/fed/TermCounts.sps", "w")
# later create a python program that extracts all words instead of
using NoteTab
termfile = open("C:/Users/Art/Desktop/fed/allWords.txt")
termlist = termfile.readlines()
termlist = [item.rstrip("\n") for item in termlist]
print len(termlist)
# check for SPSS reserved words
varnames = textwrap.wrap(" ".join([v.lower() in ['and', 'or',
'not', 'eq', 'ge',
'gt', 'le', 'lt', 'ne', 'all', 'by', 'to','with'] and (v+"_r") or v
for v in termlist]))
syntaxfile.write("data list file=
'c:/users/Art/desktop/fed/termcounts.txt' free/docnumber\n")
syntaxfile.writelines([v + "\n" for v in varnames])
syntaxfile.write(".\n")
# before using the syntax manually replace spaces internal to a
string to underscore // replace (ltrtim(rtrim(varname))," ","_")
replace any special characters with @ in variable names
for p in range(len(papers)):
range(len()) is un-pythonic. Simply do
for paper in papers:
and of course use paper below instead of papers[p]
counts = []
for t in termlist:
counts.append(len(re.findall(r"\b" + t + r"\b", papers[p],
re.IGNORECASE)))
if sum(counts) > 0:
papernum = re.search("[0-9]+", papers[p]).group(0)
countsfile.write(str(papernum) + " " + " ".join([str(s) for s
in counts]) + "\n")
Art
If you're memory limited, you really should sequence through the
files, only loading one at a time, rather than all at once. It's no
harder. Use dirlist() to make a list of files, then your loop
becomes something like:
for infile in filelist:
paper = " ".join(open(infile, "r").readlines())
Naturally, to do it right, you should use with... Or at least
close each file when done.
DaveA
Thank you for getting back to me. I am trying to generalize a process
that 50 years ago used 30 terms on the whole file and I am using the
task of generalizing the process to learn python. In the post I
sent there were comments to myself about things that I would want to
learn about. One of the first is to learn about processing all files
in a folder, so your reply will be very helpful. It seems that
dirlist() should allow me to include the filespec in the output file
which would be very helpful.
to rephrase my questions.
Is there a way to tell python to use more RAM?
Does python use the same array space over as it counts the
occurrences for each input document? Or does it keep every row of the
output someplace even after it has written it to the output? If it
does keep old arrays, is there a way to "close" the output array in
RAM between documents
I narrowed down the problem. With 4035 terms it runs OK. With 4040
the end of the output matrix is messed up. I do not think it is a
limit of my resources that gets in the way. I have 352G of free hard
disk if it goes virtual. I have 8G of RAM. Even if python turns
out to be strictly 32Bit I think it would be able to use 3G of RAM.
The input file is 1.1M so that should be able to fit in RAM many times.
P.S. I hope I remembered correctly that this list put replies at the
bottom.
Art
Python comes in 32 and 64 bit versions, so it depends on which you're
running. A 32bit executable under Windows is restricted to 2gb,
regardless of physical RAM or disk capacity. There is a way to
configure that in boot.ini to use 3gb instead, but it doesn't work in
all circumstances. Perhaps in 64bit Windows, it lets you use 3gb.
I'm not so sure your problem has anything to do with memory, however.
If your total input is under 2meg, then it's almost certainly not.
But you could get some ideas by examining the len(papers) as I said,
and also len(alltext)
You ask how to free memory. I'll assume you're using CPython, perhaps
version 2.6. If you set a variable to None, it'll free the previous
object it pointed at. So when you're done with alltext, you can
simply set it to None. Or use the "del" statement, which also frees
the name itself. That's already the case with your loop, with the
counts variable. Each time through the loop, it gets reassigned to
[], freeing the previous counts entirely.
If your problem were indeed memory, you could process one file at a
time, and cut it by some 80-fold. And if that's not enough, you
could process each file one line at a time.
You should be able to find your real bug with a judicious use of
prints. Are you actually looping through that final for loop 87
times? Or maybe some files don't begin with the word FEDERALIST ? or
some files don't have any matches. (To check that, add an else clause
for your if sum().
DaveA
Dave,
Thank you.
87 is what print len(papers) puts on the screen at the beginning of the
run. There are 86 papers in the file.
I checked and each paper starts with "FEDERALIST No."
When I use the 30 original terms, or the 70 used later by others, the
output data has the correct document numbers, 1-69, 2 versions of 70,
and 71 to 85 in the 86 rows of the output. (which is what I see when I
read the text into a word processor). Also the number of output lines
that do not start with the correct document number increases as the
number of terms increases past 4035. 4045 and 4045 have 84 lines start
correctly. 8000 terms has only the first document number read correctly.
I make no changes to the python code when I run with a longer list of
terms. I make no changes to the original txt file I received. All I
change is the number of terms in allWords.txt. All of the longer lists
of terms include the terms on the shorter list so counts should not be
sparser with a longer list of terms to count. All papers should have
some counts.
I checked and the python screen says
Python 2.6.2 (r262:71605, Apr 14 2009, 22:46:50) [MSC v.1500 64 bit
(AMD64)] on win32
so RAM cannot be the problem.
I'll cut the big file down into 1 paper per file, put the paper number
into the name of the file, and try that. I only need the papers
concatenated to get the list of all words that occur in any file. Right
now I use NoteTab to cut and paste that list anyways so I don't need to
have 1 big file for python. (As I learn python a later task would be to
generate that list via python.)
BTW is Python some kind of a grandchild to Algol which was around in the
early 70's? It seems reminiscent.
Art
_______________________________________________
Tutor maillist - Tutor@python.org
To unsubscribe or change subscription options:
http://mail.python.org/mailman/listinfo/tutor
