mapfilter

March 3, 2009 at 5:06 pm (erlang, programming) (, , , )

Have you ever mapped a list, then filtered it? Or filtered first, then mapped? Why not do it all in one pass with mapfilter?

mapfilter?

mapfilter is a function that combines the traditional map & filter of functional programming by using the following logic:

  1. if your function returns false, then the element is discarded
  2. any other return value is mapped into the list

Why?

Doing a map and then a filter is O(2N), whereas mapfilter is O(N). That’s twice as a fast! If you are dealing with large lists, this can be a huge time saver. And for the case where a large list contains small IDs for looking up a larger data structure, then using mapfilter can result in half the number of database lookups.

Obviously, mapfilter won’t work if you want to produce a list of boolean values, as it would filter out all the false values. But why would you want to map to a list booleans?

Erlang Code

Here’s some erlang code I’ve been using for a while:

mapfilter(F, List) -> lists:reverse(mapfilter(F, List, [])).

mapfilter(_, [], Results) ->
        Results;
mapfilter(F, [Item | Rest], Results) ->
        case F(Item) of
                false -> mapfilter(F, Rest, Results);
                Term -> mapfilter(F, Rest, [Term | Results])
        end.

Has anyone else done this for themselves? Does mapfilter exist in any programming language? If so, please leave a comment. I think mapfilter is a very simple & useful concept that should be a included in the standard library of every (functional) programming language. Erlang already has mapfoldl (map-reduce in one pass), so why not also have mapfilter?

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Chunk Extraction with NLTK

February 23, 2009 at 4:02 pm (programming, python) (, , , , , )

Chunk extraction is a useful preliminary step to information extraction, that creates parse trees from unstructured text. Once you have a parse tree of a sentence, you can do more specific information extraction, such as named entity recognition and relation extraction.

Chunking is basically a 3 step process:

  1. Tag a sentence
  2. Chunk the tagged sentence
  3. Analyze the parse tree to extract information

I’ve already written about how to train a part of speech tagger and a chunker, so I’ll assume you’ve already done the training, and now you want to use your tagger and chunker to do something useful.

Tag Chunker

The previously trained chunker is actually a chunk tagger. It’s a Tagger that assigns IOB chunk tags to part-of-speech tags. In order to use it for proper chunking, we need some extra code to convert the IOB chunk tags into a parse tree. I’ve created a wrapper class that complies with the nltk ChunkParserI interface and uses the trained chunk tagger to get IOB tags and convert them to a proper parse tree.

import nltk.chunk
import itertools

class TagChunker(nltk.chunk.ChunkParserI):
    def __init__(self, chunk_tagger):
        self._chunk_tagger = chunk_tagger

    def parse(self, tokens):
        # split words and part of speech tags
        (words, tags) = zip(*tokens)
        # get IOB chunk tags
        chunks = self._chunk_tagger.tag(tags)
        # join words with chunk tags
        wtc = itertools.izip(words, chunks)
        # w = word, t = part-of-speech tag, c = chunk tag
        lines = [' '.join([w, t, c] for (w, (t, c)) in wtc if c]
        # create tree from conll formatted chunk lines
        return nltk.chunk.conllstr2tree('\n'.join(lines))

Chunk Extraction

Now that we have a proper chunker, we can use it to extract chunks. Here’s a simple example that tags a sentence, chunks the tagged sentence, then prints out each noun phrase.

# sentence should be a list of words
tagged = tagger.tag(sentence)
tree = chunker.parse(tagged)
# for each noun phrase sub tree in the parse tree
for subtree in tree.subtrees(filter=lambda t: t.node == 'NP'):
    # print the noun phrase as a list of part-of-speech tagged words
    print subtree.leaves()

Each sub tree has a phrase tag, and the leaves of a sub tree are the tagged words that make up that chunk. Since we’re training the chunker on IOB tags, NP stands for Noun Phrase. As noted before, the results of this natural language processing are heavily dependent on the training data. If your input text isn’t similar to the your training data, then you probably won’t be getting many chunks.

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Test Driven Development in Python

February 5, 2009 at 8:46 am (programming, python) (, , , , , , )

One of my favorite aspects of Python is that it makes practicing TDD very easy. What makes it so frictionless is the doctest module. It allows you to write a test at the same time you define a function. No setup, no boilerplate, just write a function call and the expected output in the docstring. Here’s a quick example of a fibonacci function.

def fib(n):
        '''Return the nth fibonacci number.
        >>> fib(0)
        0
        >>> fib(1)
        1
        >>> fib(2)
        1
        >>> fib(3)
        2
        >>> fib(4)
        3
        '''
        if n == 0:
                return 0
        elif n == 1:
                return 1
        else:
                return fib(n - 1) + fib(n - 2)

If you want to run your doctests, just add the following three lines to the bottom of your module.

if __name__ == '__main__':
        import doctest
        doctest.testmod()

Now you can run your module to run the doctests, like python fib.py.

So how well does this fit in with the TDD philosophy? Here’s the basic TDD practices.

  1. Think about what you want to test
  2. Write a small test
  3. Write just enough code to fail the test
  4. Run the test and watch it fail
  5. Write just enough code to pass the test
  6. Run the test and watch it pass (if it fails, go back to step 4)
  7. Go back to step 1 and repeat until done

And now a step-by-step breakdown of how to do this with doctests, in excruciating detail.

1. Define a new empty method

def fib(n):
        '''Return the nth fibonacci number.'''
        pass

if __name__ == '__main__':
        import doctest
        doctest.testmod()

2. Write a doctest

def fib(n):
        '''Return the nth fibonacci number.
        >>> fib(0)
        0
        '''
        pass

3. Run the module and watch the doctest fail

python fib.py
**********************************************************************
File "fib1.py", line 3, in __main__.fib
Failed example:
    fib(0)
Expected:
    0
Got nothing
**********************************************************************
1 items had failures:
   1 of   1 in __main__.fib
***Test Failed*** 1 failures.

4. Write just enough code to pass the failing doctest

def fib(n):
        '''Return the nth fibonacci number.
        >>> fib(0)
        0
        '''
        return 0

5. Run the module and watch the doctest pass

python fib.py

6. Go back to step 2 and repeat

Now you can start filling in the rest of function, one test at time. In practice, you may not write code exactly like this, but the point is that doctests provide a really easy way to test your code as you write it.

Unit Tests

Ok, so doctests are great for simple tests. But what if your tests need to be a bit more complex? Maybe you need some external data, or mock objects. In that case, you’ll be better off with more traditional unit tests. But first, take a little time to see if you can decompose your code into a set of smaller functions that can be tested individually. I find that code that is easier to test is also easier to understand.

Running Tests

For running my tests, I use nose. I have a tests/ directory with a simple configuration file, nose.cfg

[nosetests]
verbosity=3
with-doctest=1

Then in my Makefile, I add a test command so I can run make test.

test:
        @nosetests --config=tests/nose.cfg tests PACKAGE1 PACKAGE2

PACKAGE1 and PACKAGE2 are optional paths to your code. They could point to unit test packages and/or production code containing doctests.

And finally, if you’re looking for a continuous integration server, try Buildbot.

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Programming as Design

January 7, 2009 at 12:19 pm (design, programming) (, , , , )

Some say programming is engineering, others call it an art. A few might (mistakenly) think it’s a science. But both the art and engineering can be encapsulated under the umbrella of design. The best design is functional art, and a huge part of the artistic beauty of a product is a result of carefully engineered functionality. Products that are not carefully designed and engineered generally suck to use, and that applies to everything from cameras to software APIs.

Design Principles

There are 4 major principles of graphic design.

  1. alignment
  2. proximity
  3. contrast
  4. consistency / repetition

Alignment

The principle of alignment is that everything on a page should be connected to something else on the page. The goal of alignment in graphic design is to create visual associations, often using a grid based layout. In software, we can apply this principle to the connectedness of data, such as the object inheritance hierarchy and relational data structures. Ideally, all your objects fit nicely into a well-defined hierarchy and your data structures relate to each other in an intuitive fashion. Of course, the real world of programming is never as clean as you’d like, but keep this principle in mind whenever you create a new object, add a new dependency, or modify relational structures.

  • Does the object cleanly fit within the existing hierarchy? If not, do you need to change the new object, or re-align the hierarchy?
  • How does this data structure relate to that other data structure? What will happen if the relations change? Will you need to re-align the relational structures?

Keeping your objects and data structures neatly aligned will result in easier to understand relations and hierarchies.

Proximity

The principle of proximity is that related items should be grouped together. Grouping things together is simple way to show relatedness. In software development, that generally means putting related functions into the same module, and related modules into the same package. Helper functions should be located near the functions that call them. Basically, group blocks of code in a logically consistent manner. And if possible, put documentation and tests close to the code too (python’s doctest module provides a great way to do that). One of the major benefits of following this principle is that it reduces the amount of time you’ll spend searching thru and understanding your own code. If all your code is organized in logical groups, and related functions are near each other in the same file, then it’s much easier to find a particular block of code.

Contrast

The principle of contrast is that if two things are not the same, then make them very different. The goal with contrast is to make different things distinctive from each other. Naming is great place to apply this principle. Names are all you have to distinguish between objects, functions, variables, and modules, so make sure that your names are distinctive and descriptive. Good names can tell you exactly what something is, and even imply its properties and behavior. Use different naming styles for different types of things. Private variables could be prefixed with an underscore, like _private, versus public variables like public, and CamelCase class names, as in MyClassName. Having a distinctive naming style lets you know at a glance whether something is a class, variable, or function, making your code much more readable. Whatever naming style you choose, use it consistently.

Consistency

The principle of consistency, or repetition, is that you repeat design elements. Repetition helps patterns become internalized and instantly recognizable. For programming, that means keeping a consistent code style, with consistent naming practices. Also, try to use well known standard conventions and protocols, shared libraries, and design patterns. Your code should make sense, or at least be readable, to those familiar with the language and domain. You’re not just writing code for yourself, you might be writing code for other programmers, maybe your manager, but most importantly, you’re writing code for your future self. It always sucks coming back to code you haven’t touched in months and not knowing what the hell is going on. Consistent style and software design can save you from that headache.

Programming as Design

If all this seems like obvious common sense to you, then great! But common sense isn’t always so common. The point of this article is make you aware that everyday software programming is filled with design choices. Naming a variable is a design choice. Creating a new module is a design choice. The layout of your working directory is a design choice. Be conscious of these choices and use the above principles and to inform your decisions. The choices you make communicate how the software works and how the code fits together. Make every choice deliberate and justifiable. Use refactoring to improve the design without affecting the functionality.

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