Alphabet Race - a kid's game

TL;DR: I built a little truck "game" to teach my son alphabets.

I started watching http://javascript30.com a couple of days ago. It's a series of 30 videos that uses vanilla Javascript to do amazing things.

I have only done the first two videos in the series. Based on what I learned in those two videos, I decided to write a small game. My son is a fan of trucks, so I cobbled together a truck race that teaches him alphabets.

The game is written using the latest version of Javascript. It will not work in old browsers (sorry). It is not designed for tablets or phones, you need a keyboard to play this game.

Game: http://alphabetrace.itsybits.xyz/

Source: https://github.com/amjith/alphabet_race/

My goal was to create an educational game that is entertaining but not addictive. 

You can send me feedback about the game via twitter or email.

Nostalgic Programming

For some unknown reason, I looked up GWBasic today and downloaded an emulator. GWBasic was the first programming language that I learned. I have fond memories of that language. I love the fact that I could switch to a graphics mode and start drawing circles and squares. 

After about 30 minutes of fumbling around, my muscle memory kicked in and I started to write a simple program to draw some shapes on the screen. I asked Sempi to sit with me and help me with the drawing. He wanted me to draw a truck, so I decided to give it a shot. 

He lost interest midway when I started looking up various commands in the programming manual, but I stuck with it. 

Here's the creation in all it's glory. 

Needless to say, I had a lot of fun. 

FuzzyFinder - in 10 lines of Python

Introduction:

FuzzyFinder is a popular feature available in decent editors to open files. The idea is to start typing partial strings from the full path and the list of suggestions will be narrowed down to match the desired file. 

Examples: 

Vim (Ctrl-P)

Sublime Text (Cmd-P)

This is an extremely useful feature and it's quite easy to implement.

Problem Statement:

We have a collection of strings (filenames). We're trying to filter down that collection based on user input. The user input can be partial strings from the filename. Let's walk this through with an example. Here is a collection of filenames:

When the user types 'djm' we are supposed to match 'django_migrations.py' and 'django_admin_log.py'. The simplest route to achieve this is to use regular expressions. 

Solutions:

Naive Regex Matching:

Convert 'djm' into 'd.*j.*m' and try to match this regex against every item in the list. Items that match are the possible candidates.

This got us the desired results for input 'djm'. But the suggestions are not ranked in any particular order.

In fact, for the second example with user input 'mig' the best possible suggestion 'migrations.py' was listed as the last item in the result.

Ranking based on match position:

We can rank the results based on the position of the first occurrence of the matching character. For user input 'mig' the position of the matching characters are as follows:

Here's the code:

We made the list of suggestions to be tuples where the first item is the position of the match and second item is the matching filename. When this list is sorted, python will sort them based on the first item in tuple and use the second item as a tie breaker. On line 14 we use a list comprehension to iterate over the sorted list of tuples and extract just the second item which is the file name we're interested in.

This got us close to the end result, but as shown in the example, it's not perfect. We see 'main_generator.py' as the first suggestion, but the user wanted 'migration.py'.

Ranking based on compact match:

When a user starts typing a partial string they will continue to type consecutive letters in a effort to find the exact match. When someone types 'mig' they are looking for 'migrations.py' or 'django_migrations.py' not 'main_generator.py'. The key here is to find the most compact match for the user input.

Once again this is trivial to do in python. When we match a string against a regular expression, the matched string is stored in the match.group(). 

For example, if the input is 'mig', the matching group from the 'collection' defined earlier is as follows:

We can use the length of the captured group as our primary rank and use the starting position as our secondary rank. To do that we add the len(match.group()) as the first item in the tuple, match.start() as the second item in the tuple and the filename itself as the third item in the tuple. Python will sort this list based on first item in the tuple (primary rank), second item as tie-breaker (secondary rank) and the third item as the fall back tie-breaker. 

This produces the desired behavior for our input. We're not quite done yet.

Non-Greedy Matching

There is one more subtle corner case that was caught by Daniel Rocco. Consider these two items in the collection ['api_user', 'user_group']. When you enter the word 'user' the ideal suggestion should be ['user_group', 'api_user']. But the actual result is:

Looking at this output, you'll notice that api_user appears before user_group. Digging in a little, it turns out the search user expands to u.*s.*e.*r; notice that user_group has two rs, so the pattern matches user_gr instead of the expected user. The longer match length forces the ranking of this match down, which again seems counterintuitive. This is easy to change by using the non-greedy version of the regex (.*? instead of .*) on line 4. 

Now that works for all the cases we've outlines. We've just implemented a fuzzy finder in 10 lines of code.

Conclusion:

That was the design process for implementing fuzzy matching for my side project pgcli, which is a repl for Postgresql that can do auto-completion. 

I've extracted fuzzyfinder into a stand-alone python package. You can install it via 'pip install fuzzyfinder' and use it in your projects.

Thanks to Micah Zoltu and Daniel Rocco for reviewing the algorithm and fixing the corner cases.

If you found this interesting, you should follow me on twitter

Epilogue:

When I first started looking into fuzzy matching in python, I encountered this excellent library called fuzzywuzzy. But the fuzzy matching done by that library is a different kind. It uses levenshtein distance to find the closest matching string from a collection. Which is a great technique for auto-correction against spelling errors but it doesn't produce the desired results for matching long names from partial sub-strings.

Python Profiling - Part 1

I gave a talk on profiling python code at the 2012 Utah Open Source Conference. Here are the slides and the accompanying code.

There are three parts to this profiling talk:

  • Standard Lib Tools - cProfile, Pstats
  • Third Party Tools - line_profiler, mem_profiler
  • Commercial Tools - New Relic

This is Part 1 of that talk. It covers:

  • cProfile module - usage
  • Pstats module - usage
  • RunSnakeRun - GUI viewer

Why Profiling:

  • Identify the bottle-necks.
  • Optimize intelligently. 

In God we trust, everyone else bring data

cProfile:

cProfile is a profiling module that is included in the Python's standard library. It instruments the code and reports the time to run each function and the number of times each function is called. 

Basic Usage:

The sample code I'm profiling is finding the lowest common multiplier of two numbers. lcm.py

# lcm.py - ver1 
    def lcm(arg1, arg2):
        i = max(arg1, arg2)
        while i < (arg1 * arg2):
            if i % min(arg1,arg2) == 0:
                return i
            i += max(arg1,arg2)
        return(arg1 * arg2)

    lcm(21498497, 3890120)

Let's run the profiler.

$ python -m cProfile lcm.py 
     7780242 function calls in 4.474 seconds
    
    Ordered by: standard name
   
    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
         1    0.000    0.000    4.474    4.474 lcm.py:3()
         1    2.713    2.713    4.474    4.474 lcm.py:3(lcm)
   3890120    0.881    0.000    0.881    0.000 {max}
         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
   3890119    0.880    0.000    0.880    0.000 {min}

Output Columns:

  • ncalls - number of calls to a function.
  • tottime - total time spent in the function without counting calls to sub-functions.
  • percall - tottime/ncalls
  • cumtime - cumulative time spent in a function and it's sub-functions.
  • percall - cumtime/ncalls

It's clear from the output that the built-in functions max() and min() are called a few thousand times which could be optimized by saving the results in a variable instead of calling it every time. 

    Pstats:

    Pstats is also included in the standard library that is used to analyze profiles that are saved using the cProfile module. 

    Usage:

    For scripts that are bigger it's not feasible to analyze the output of the cProfile module on the command-line. The solution is to save the profile to a file and use Pstats to analyze it like a database. Example:  Let's analyze shorten.py.

    $ python -m cProfile -o shorten.prof shorten.py   # saves the output to shorten.prof
    
    $ ls
    shorten.py shorten.prof

    Let's analyze the profiler output to list the top 5 frequently called functions.

    $ python 
    >>> import pstats
    >>> p  = pstats.Stats('script.prof')   # Load the profiler output
    >>> p.sort_stats('calls')              # Sort the results by the ncalls column
    >>> p.print_stats(5)                   # Print top 5 items
    
        95665 function calls (93215 primitive calls) in 2.371 seconds
        
       Ordered by: call count
       List reduced from 1919 to 5 due to restriction <5>
        
           ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        10819/10539    0.002    0.000    0.002    0.000 {len}
               9432    0.002    0.000    0.002    0.000 {method 'append' of 'list' objects}
               6061    0.003    0.000    0.003    0.000 {isinstance}
               3092    0.004    0.000    0.005    0.000 /lib/python2.7/sre_parse.py:182(__next)
               2617    0.001    0.000    0.001    0.000 {method 'endswith' of 'str' objects}

    This is quite tedious or not a lot of fun. Let's introduce a GUI so we can easily drill down. 

    RunSnakeRun:

    This cleverly named GUI written in wxPython makes life a lot easy. 

    Install it from PyPI using (requires wxPython)

    $ pip install SquareMap RunSnakeRun
    $ runsnake shorten.prof     #load the profile using GUI

    The output is displayed using squaremaps that clearly highlights the bigger pieces of the pie that are worth optimizing. 

    It also lets you sort by clicking the columns or drill down by double clicking on a piece of the SquareMap.

    Conclusion:

    That concludes Part 1 of the profiling series. All the tools except RunSnakeRun are available as part of the standard library. It is essential to introspect the code before we start shooting in the dark in the hopes of optimizing the code.

    We'll look at line_profilers and mem_profilers in Part 2. Stay tuned. 

    You are welcome to follow me on twitter (@amjithr).

    Memoization Decorator

    Recently I had the opportunity to give a short 10 min presentation on Memoization Decorator at our local UtahPython Users Group meeting. 

    Memoization: 

    • Everytime a function is called, save the results in a cache (map).
    • Next time the function is called with the exact same args, return the value from the cache instead of running the function.

    The code for memoization decorator for python is here: http://wiki.python.org/moin/PythonDecoratorLibrary#Memoize

    Example:

    The typical recursive implementation of fibonacci calculation is pretty inefficient O(2^n).   

    def fibonacci(num):
            print 'fibonacci(%d)'%num
            if num in (0,1):
                return num
            return fibonacci(num-1) + fibonacci(num-2)

    >>> math_funcs.fibonacci(4) # 9 function calls fibonacci(4) fibonacci(3) fibonacci(2) fibonacci(1) fibonacci(0) fibonacci(1) fibonacci(2) fibonacci(1) fibonacci(0) 3

    But the memoized version makes it ridiculously efficient O(n) with very little effort.

    import memoized
    @memoized
    def fibonacci(num):
        print 'fibonacci(%d)'%num
        if num in (0,1):
            return num
        return fibonacci(num-1) + fibonacci(num-2)
        
    >>> math_funcs.mfibonacci(4)  # 5 function calls
        fibonacci(4)
        fibonacci(3)
        fibonacci(2)
        fibonacci(1)
        fibonacci(0)
        3

    We just converted an algorithm from Exponential Complexity to Linear Complexity by simply adding the memoization decorator.

    Slides:

    Presentation:

    I generated the slides using LaTeX Beamer. But instead of writing raw LaTeX code I used reStructured Text (rst) and used rst2beamer script to generate the .tex file. 

    Source:

    The rst file and tex files are available in Github.

    https://github.com/amjith/User-Group-Presentations/tree/master/memoization_de...