[Tutor] Is the difference in outputs with different size input lists due to limits on memory with PYTHON?

[Dave Angel]

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