Poorly implementing caching in python - eye opener

So after looking at some slides on caching function returns in javascript, I was keen on trying out so in Python. And LOL I came up with this logic -

fun(val):
  if val in cache.keys():
    return cache[val]
  else:
    do the right thing...

But it seems, though "if val in cache.keys()" sounds very human friendly, it definitely would suck for a very big cache, and so it does in the following test.

I guess I'm not using timeit in the classical way where I would pass some statements in string and ask it to do them for N number of times, but it seems passing some variables from existing code to statement string is a pain, tried global etc, didn't work. Hence a simple time diff test.

# -*- coding: utf-8 -*-
import random
from timeit import Timer

class PlainColorParser:
    def parse(self,value):
        rgb = int(value[1:], 16)
        r = rgb >> 16 & 0xff
        g = rgb >>  8 & 0xff
        b = rgb & 0xff
        return (r,g,b)

    def __call__(self,value):
        return self.parse(value)

class NoBitColorParser:
    def __call__(self,value):
        return (int(value[1:3],16), int(value[3:5],16), int(value[5:7],16))

class PoorCachedColorParser(PlainColorParser):
    def __init__(self):
        self.cache = {}

    def __call__(self,value):
        if value in self.cache.keys():
            return self.cache[value]
        self.cache[value] = self.parse(value)
        return self.cache[value]

class CachedExceptionColorParser(PlainColorParser):
    def __init__(self):
        self.cache = {}

    def __call__(self,value):
        try:
            return self.cache[value]
        except KeyError:
            self.cache[value] = self.parse(value)
            return self.cache[value]

class CachedColorParser(PlainColorParser):
    def __init__(self):
        self.cache = {}

    def __call__(self,value):
        if value in self.cache:
            return self.cache[value]
        else:
            self.cache[value] = self.parse(value)
            return self.cache[value]

if __name__ == "__main__":
    t = Timer()
    pccParse = PoorCachedColorParser()
    cecParse = CachedExceptionColorParser()
    ccParse = CachedColorParser()
    pcParse = PlainColorParser()
    nbParse = NoBitColorParser()

    # setup some random data to test
    colors = []
    for i in xrange(100000):
        colors.append("#" + hex(random.randint(0xfe0000, 0xff0aff))[2:])


    def timeDiff(obj):
        start = t.timer()
        for c in colors:
            obj(c)
        stop = t.timer()
        return ((1000000*stop - 1000000*start)/1000000)

    # ---- test poorly cached
    print "Poorly Cached - %.2fs" % timeDiff(pccParse)

    # ---- test exception cached
    print "Exception Cached - %.2fs" % timeDiff(cecParse)

    # ---- test cached
    print "Cached - %.2fs" % timeDiff(ccParse)

    # ---- test uncached
    print "Non Cached - %.2fs" % timeDiff(pcParse)

    # ---- test no bitwise, uncached
    print "Not Bitwise, Non Cached - %.2fs" % timeDiff(nbParse)

So we've got 4 5 classes to represent different ways of parsing an html hex code for color e.g. "#f00f00" into a tuple of (r,g,b) integers. PlainColorParser and NoBitColorParser could easily be functions with no need of classes over them as they do not cache, but to bring them a little equal to other two cached ones, I've bound them in classes.

NoBitColorParser does string manipulations and parses 3 times before returning a tuple. PlainColorParser does better than that, it uses bit shifts and AND masks to filter out content after 1 round of parsing integer from string. PoorCachedColorParser does caching in an obvious way "if its there in cache keys... else ...", and CachedColorParser CachedExceptionColorParser complies to the philosophy of "Fail early, fail often", which is quite interesting :D
but recent findings reveal that CachedColorParser is the right, fast, pythonic way.

What the test does is - generate 100000 random color codes, pick them from a range of 2816 colors (0xff0aff - 0xff0000 + 1). Obviously many colors are bound to get repeated says pigeon hole.

Here's what goes on in an average run on my machine -

abhishekmishra@mbp [~/code]> python pycaching.py
Poorly Cached - 340.23s
Exception Cached - 0.30s
Cached - 0.18s
Non Cached - 0.15s
Not Bitwise, Non Cached - 0.16s

"if value in self.cache.keys():" in PoorCachedColorParser gives you a thumbs down with a sucky performance, obviously not the right thing to do!!! (I was mistaken)

CachedExceptionColorParser gives a sweet 0.30s, "Fail early, Fail often" works :)
But wait, CachedColorParser goes further even a bit more with just 0.18s.

NoBitColorParser is suckier than the Non Cached PlainColorParser, which points out that string ops, parsing integers is one costly affair.

So much for food to my sleeplessness. Oh I remember doing something similar in PyMos too :D.

More updates -

Looking at Dhananjay's code on BangPypers, I think I was too excited to throw in the idion of fail fast in this place, it can albeit be done in a cleaner way. So instead of -

try:
    return self.cache[value]
except KeyError:
    self.cache[value] = self.parse(value)
    return self.cache[value]

You could just write in much cleaner way -

if value in self.cache:
    return self.cache[value]
else:
    self.cache[value] = self.parse(value)
    return self.cache[value]

So the issue was with cache.keys(), which now seems like an obvious slow and shitty way.
New stats reveal that even Try: Except:... fail fast is even not the right way.

Notice the almost double time difference between "try.. except.." way and "if value in self.cache".

Lesson learnt :)

Optimization by Memoization ( not memorization )

To iterate is Human, to Recurse, to Recurse, Divine - unknown

Solving COINS at spoj was a great lesson in recursion. Beautiful may the recursive code seem, but in some cases it is slow as a snail in worst case scenario. Consider finding nth Fibonacci by recursion as an example! And in some cases as in finding p^q, recursion is truly divine god's gift ;P.
Same is the case with COINS, its pretty easy to figure out the function that gets the job done... but give it something like n = 1000000000, poof! it vanishes into a deep slumber!
So, how can we make it work faster... ?
Have you ever noticed that, kids are asked to remember multiplication tables from 2 to 10 when in primary mathematics classes! Ever wondered why to do this mugging up at such a tender age. The answer is in... how we multiply two big numbers (abcd * pqrs). Here every digit i & j is in range of 0 through 9. So, if you've memorized multiplication table for 2 to 9, it will be very easy to get through (abcd * pqrs).
Thats exactly what we will do here. Basically as the problem gets solved for bigger numbers, loads of steps trickle down to maxm() function for small numbers, just like end of roots of a tree. So, here again we will pre-calculate and store maxm(n) in an array for 1<n<9400. Why 9400? Have a look at http://www.spoj.pl/problems/COINS/, you can't write more that 50KB of code! So this weekend I decide to see the real consequence of memoization. Here's the allegro code I wrote to plot time against my COIN's solution. Memoization 9400 values works for the spoj judge. And it seems even memorizing 450 values works amazingly for my Athlon 64 :P

#include <iostream>
#include <allegro.h>
#include <time.h>

using namespace std;
unsigned int 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double lim = 0;

unsigned int maxm(unsigned int n){
// 9400 - size of array
if (n<lim)
 return a[n];

unsigned int p;
p = n/2 + n/3 + n/4;

if (p>n)
 //sort of binary recursion which you should figure out to solve COINS
else
 return n;
}

int main (){
unsigned int n=10,col2,col;
double x=0,px=1,py=760,y;
clock_t start ,stop;

allegro_init();
set_color_depth(32);
set_gfx_mode (GFX_AUTODETECT_WINDOWED,1024,768,0,0);

clear_to_color (screen,makecol(0,0,0));

while (lim<450){
 col = makecol ((lim/450)*250+5,  (lim/450)*255, 100);
 col2=makecol (((lim/450)*250+5)/3,  (lim/450)*255/3, 100/3);
 while (n<310){
  n+=1;
  start = clock();
  maxm(n*20000);
  stop = clock();
  //  cout << stop <<" "<<start << endl;
  y=760-((stop-start)*5);
  rectfill (screen,px,760,px+3,y,col2);
  line (screen,px,py,px+3,y,col);
 
  px+=3;
  py=y;
 }
 lim=lim+5;
 x=0; px=1; py=760;
 n=10;
 cout << lim<<endl;
}
getchar();
}
END_OF_MAIN();

And here's the plot! amazing visible differences...

checkout the drastic improvement from lim=0 to lim=450!

That's all for the Friday research folks!