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.

Pycast - Python screencasts

Pycast - Weekly screencasts on Python and DataScience by Matt Harrison. 

Matt is bootstrapping pycast through kickstarter. I'm excited about it because I've attended Matt's tutorials and came away feeling leveled up on my Python chops. 

Nearly 5 years ago I was getting started in Python and learning on my own by writing small scripts to automate silly stuff. I wasn't writing anything adventurous and I was looking for a way to improve my skills.

Right around that time I started getting involved in the open source community in Utah and decided to go to a local conference. Matt was doing a 3 hour tutorial that covered beginner to intermediate Python. When the session was over I felt empowered. I couldn't wait to get back home to do the exercises that he had laid out during the training. After working through them I felt like I really knew the language. I was writing generators and decorators by the end of it. It was an accelerated learning experience that took me from a novice to a journeyman

The beauty of his training is, it wasn't merely a brain dump, he was teaching me to how to learn, where to look up the docs, how to recognize idiomatic python and best practices of programming. 

I eventually landed a job doing full time Python at an awesome company.

That's why I'm excited about his new venture. This is a great opportunity for me to dive into Data Science and I can't wait to see his videos and workout the exercises.

If you're still on the fence about it, leave a comment on his kickstarter page with your question. He's a friendly and responsive person.

Launching pgcli

I've been developing pgcli for a few months now. 

It is now finally live http://pgcli.com

It all started when Jonathan Slenders sent me a link to his side-project called python-prompt-toolkit

I started playing around with it to write some toy programs. Then I wrote a tutorial for how to get started with prompt_toolkit https://github.com/jonathanslenders/python-prompt-toolkit/tree/master/examples/tutorial

Finally I started writing something more substantial to scratch my own itch. I was dealing with Postgres databases a lot at that time. The default postgres client 'psql' is a great tool, but it lacked auto-completion as I type and it was quite bland (no syntax highlighting). So I decided to take this as my opportunity to write an alternate. 

Thus the creatively named project 'pgcli' was born.

Details about pgcli.com:

It is built using pelican a static site generator written in Python. 

It is hosted by Github pages. 

The content is written using RestructuredText.

Inspiration:

The design inspiration for the tool comes from my favorite python interpreter bpython.

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...

     

    Productive Meter

    A few weeks ago I decided that I should suck it up and start learning how to develop for the web. After asking around, my faithful community brethren, I decided to learn Django from its docs

    ::Django documentation is awesome::

    Around this time I came across this post about Waking up at 5am to code. I tried it a few times and it worked wonders. I've been working on a small project that can keep track of my productivity on the computer. The concept is really simple, just log the window that is on top and find a way to display that data in a meaningful way. 

    Today's 5am session got me to a milestone on my project. I am finally able to visaulize the time I spend using a decent looking graph. Which is a huge milestone for someone who learned how to display html tables 3 weeks ago.

    Tools:

    A huge thanks to my irc friends and random geeks who wrote awesome blog posts and SO answers on every problem I encountered.

    I will be open-sourcing the app pretty soon. Stay tuned.

    Picking 'k' items from a list of 'n' - Recursion

    Let me preface this post by saying I suck at recursion. But it never stopped me from trying to master it. Here is my latest (successful) attempt at an algorithm that required recursion. 

    Background: 

    You can safely skip this section if you're not interested in the back story behind why I decided to code this up. 

    I was listening to KhanAcademy videos on probability. I was particularly intrigued by the combinatorics video. The formula to calculate the number of combinations of nCr was simple, but I wanted to print all the possible combinations of nCr. 

    Problem Statement:

    Given 'ABCD' what are the possible outcomes if you pick 3 letters from it to form a combination without repetition (i.e. 'ABC' is the same as 'BAC'). 

    At first I tried to solve this using an iterative method and gave up pretty quickly. It was clearly designed to be a recursive problem. After 4 hours of breaking my head I finally got a working algorithm using recursion. I was pretty adamant about not looking it up online but I seeked some help from IRC (Thanks jtolds). 

    Code: 

    def combo(w, l):
            lst = []
            if l < 1:
                return lst
            for i in range(len(w)):
                if l == 1:
                    lst.append(w[i])
                for c in combo(w[i+1:], l-1):
                    lst.append(w[i] + c)
            return lst

    Output:

    >>> combinations.combo('abcde',3)
        ['abc', 'abd', 'abe', 'acd', 'ace', 'ade', 'bcd', 'bce', 'bde', 'cde']

    Thoughts:

    • It helps to think about recursion with the assumption that an answer for step n-1 already exists.
    • If you are getting partial answers check the condition surrounding the return statement.
    • Recursion is still not clear (or easy). 

    I have confirmed that this works for bigger data sets and am quite happy with this small victory.

    Python Profiling

    I did a presentation at our local Python User Group meeting tonight. It was well received, but shorter than I had expected. I should've added a lot more code examples. 

    We talked about usage of cProfile, pstats, runsnakerun and timeit. 

    Here are the slides from the presentations: 

    The slides were done using latex-beamer, but I wrote the slides in reStructuredText and used rst2beamer to create the tex file which was then converted to pdf using pdflatex. 

    The source code for the slides are available on github.

    Rapid Prototyping in Python

    I was recently assigned to a new project at work. Like any good software engineer I started writing the pseudocode for the modules. We use C++ at work to write our programs.

    I quickly realized it's not easy to translate programming ideas to English statements without a syntactic structure. When I was whining about it to Vijay, he told me to try prototyping it in Python instead of writing pseudocode. Intrigued by this, I decided to write a prototype in Python to test how various modules will come together.

    Surprisingly it took me a mere 2 hours to code up the prototype. I can't emphasize enough, how effortless it was in Python.

    What makes Python an ideal choice for prototyping:

    Dynamically typed language:

    Python doesn't require you to declare the datatype of a variable. This lets you write a function that is generic enough to handle any kind of data. For eg:

    def max_val(a,b):
        return a if a >b else b

    This function can take integers, floats, strings, a combination of any of those, or lists, dictionaries, tuples, whatever.

    A list in Python need not be homogenous. This is a perfectly good list:

    [1, 'abc', [1,2,3]]

    This lets you pack data in unique ways on the fly which can later be translated to a class or a struct in a statically typed language like C++.

    class newDataType
    {
        int i;
        String str;
        Vector vInts;
    };

    Rich Set to Data-Structures:

    Built-in support for lists, dictionaries, sets, etc reduces the time involved in hunting for a library that provides you those basic data-structures.

    Expressive and Succinct:

    The algorithms that operate on the data-structures are intuitive and simple to use. The final code is more readable than a pseudocode.

    For example: Lets check if a list has an element

    >>> lst = [1,2,3]    # Create a list
    >>> res = 2 in lst   # Check if 2 is in 'lst'
    True

    If we have to do it in C++.

    list lst;
    lst.push_back(3);
    lst.push_back(1);
    lst.push_back(7);
    list::iterator result = find(lst.begin(), lst.end(), 7); 
    bool res = (result != lst.end())

    Python Interpreter and Help System:

    This is a huge plus. The presence of interpreter not only aids you in testing snippets of code, but it acts as an help system. Lets say we want to look up the functions that operate on a List.

    >>> dir([])
    ['__add__', '__class__', '__contains__', '__delattr__', '__delitem__',
    '__delslice__', '__doc__', '__eq__', '__format__', '__ge__', 
    '__getattribute__', '__getitem__', '__getslice__', '__gt__', '__hash__',
    '__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__',
    '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',
    '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__',
    '__setslice__', '__sizeof__', '__str__', '__subclasshook__', 'append',
    'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']
    
    >>> help([].sort)
    Help on built-in function sort:
         
    sort(...)
        L.sort(cmp=None, key=None, reverse=False) -- stable sort *IN PLACE*;
        cmp(x, y) -> -1, 0, 1

    Advantages of prototyping instead of pseudocode:

    • The type definition of the datastructures emerge as we code.
    • The edge cases start to emerge when you prototype.
    • A set of required supporting routines.
    • A better estimation of the time required to complete a task.