[Chicago] Need advice on this project.

Lewit, Douglas d-lewit at neiu.edu
Tue Nov 10 12:25:57 EST 2015

10 seconds???!!!  Wow!!!  Okay then, I'll buy you dinner if you finish my
homework for me!!!   ;-)

Argument unpacking?  What's that?  As for lambdas, I just LOVE them!  They
are so cool, and make certain procedures so much easier.  What is PEP8?  It
sounds like a nutritional supplement or an energy drink!   ;-)

On Tue, Nov 10, 2015 at 9:46 AM, Adam Forsyth <adam at adamforsyth.net> wrote:

> Hi Douglas,
> You seem to post interesting homework assignments when I'm looking for a
> fun problem, thanks.
> The issue definitely isn't the performance of either Python (the language)
> or CPython (the implementation). I did the assignment last night, and
> calculating the matrix for "u1.base" took my code less than 10 seconds.
> For readability in your Correlation function, try to avoid: globals;
> creating lambdas inside loops; and indexing with constant keys rather than
> using argument unpacking (i.e. key[0]). It also helps to follow PEP8 if you
> want other Python programmers to be able to read your code easily.
> You probably have an algorithmic error in there somewhere -- it's hard for
> me to tell for sure because your code is difficult to follow. Read the
> assignment carefully, and only do what it tells you. For performance, are
> there different data structures you could use? Are there "batteries
> included" in Python that could combine some of those individual arithmetic
> operations? I don't want to be too specific here because implementing the
> algorithm is the point of the assignment.
> It looks like you still have two weeks to complete the project, so I'd
> recommend taking your time, and don't be afraid to start a new version --
> it can help you break out of bad patterns you've started in your existing
> code.
> Best,
> Adam
> On Mon, Nov 9, 2015 at 7:44 PM, Lewit, Douglas <d-lewit at neiu.edu> wrote:
>> Hey guys,
>> I need some advice on this one.  I'm attaching the homework assignment so
>> that you understand what I'm trying to do.  I went as far as the
>> construction of the Similarity Matrix, which is a matrix of Pearson
>> correlation coefficients.
>> My problem is this.  u1.base (which is also attached) contains Users
>> (first column), Items (second column), Ratings (third column) and finally
>> the time stamp in the 4th and final column.  (Just discard the 4th column.
>> We're not using it for anything. )
>> It's taking HOURS for Python to build the similarity matrix.  So what I
>> did was:
>> *head -n 5000 u1.base > practice.base*
>> and I also downloaded the PyPy interpreter for Python 3.  Then using PyPy
>> (or pypy or whatever) I ran my program on the first ten thousand lines of
>> data from u1.base stored in the new text file, practice.base.  Not a
>> problem!!!  I still had to wait a couple minutes, but not a couple hours!!!
>> Is there a way to make this program work for such a large set of data?  I
>> know my program successfully constructs the Similarity Matrix (i.e.
>> similarity between users) for 5,000, 10,000, 20,000 and even 25,000 lines
>> of data.  But for 80,000 lines of data the program becomes very slow and
>> overtaxes my CPU.  (The fan turns on and the bottom of my laptop starts to
>> get very hot.... a bad sign! )
>> Does anyone have any recommendations?  ( I'm supposed to meet with my
>> prof on Tuesday.  I may just explain the problem to him and request a
>> smaller data set to work with.  And unfortunately he knows very little
>> about Python.  He's primarily a C++ and Java programmer. )
>> I appreciate the feedback.  Thank you!!!
>> Best,
>> Douglas Lewit
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