How Weotta uses MongoDB

Grant and I will be helping 10gen celebrate the opening of their new San Francisco office on Tuesday, February 21, by talking about
How Weotta uses MongoDB. We'll cover some of our favorite features of MongoDB and how we use it for local place & events search. Then we'll finish with a preview of Weotta's upcoming MongoDB powered local search APIs.

NLTK Jam Session at NICAR 2012

On Thursday, February 23, in St Louis, MO, I'll be demonstrating how to use NLTK as part of the NewsCamp workshop at NICAR 2012. This will be a version of my PyCon NLTK Tutorial with a focus on news text and corpora like treebank.

Corpus Bootstrapping with NLTK at Strata 2012

As part of the Strata 2012 Deep Data program, I'll talk about Corpus Bootstrapping with NLTK on Tuesday, February 28. The premise of this talk is that while there's plenty of great algorithms and methods for natural language processing, most of them require a training corpus, and chances are the training corpus you really need doesn't exist. So how can you quickly create a quality corpus at minimal cost? I'll cover specific real-world examples to answer this question.

NLTK Tutorial at PyCon 2012

Introduction to NLTK will be a 3 hour tutorial at PyCon on Thursday, March 8th. You'll get to know NLTK in depth, learn about corpus organization, and train your own models manually & with nltk-trainer. My goal is that you'll walk out with at least one new NLP superpower that you can put to use immediately.

def fuzzy_match(s1, s2):
	'''
	>>> fuzzy_match('Happy Days', ' happy days ')
	True
	>>> fuzzy_match('happy days', 'sad days')
	False
	'''
	# TODO: fuzzy matching code
	return s1 == s2

Once you've got a good set of test cases, then it's much easier to tailor your fuzzy matching code to get the best results.

Normalization

The first step before doing any string matching is normalization. The goal with normalization is to transform your strings into a normal form, which in some cases may be all you need to do. While 'Happy Days' != ' happy days ', with simple normalization you can get 'Happy Days'.lower() == ' happy days '.strip().

The most basic normalization you can do is to lowercase and strip whitespace. But chances are you'll want to more. For example, here's a simple normalization function that also removes all punctuation in a string.

import string

def normalize(s):
	for p in string.punctuation:
		s = s.replace(p, '')

	return s.lower().strip()

Using this normalize function, we can make the above fuzzy matching function pass our simple tests.

def fuzzy_match(s1, s2):
	'''
	>>> fuzzy_match('Happy Days', ' happy days ')
	True
	>>> fuzzy_match('happy days', 'sad days')
	False
	'''
	return normalize(s1) == normalize(s2)

If you want to get more advanced, keep reading...

Regular Expressions

Beyond just stripping whitespace from the ends of strings, it's also a good idea replace all whitespace occurrences with a single space character. The regex function for doing this is re.sub('\s+', s, ' '). This will replace every occurrence of one or more spaces, newlines, tabs, etc, essentially eliminating the significance of whitespace for matching.

You may also be able to use regular expressions for partial fuzzy matching. Maybe you can use regular expressions to identify significant parts of a string, or perhaps split a string into component parts for further matching. If you think you can create a simple regular expression to help with fuzzy matching, do it, because chances are, any other code you write to do fuzzy matching will be more complicated, less straightforward, and probably slower. You can also use more complicated regular expressions to handle specific edge cases. But beware of any expression that takes puzzling out every time you look at it, because you'll probably be revisiting this code a number of times to tweak it for handling new cases, and tweaking complicated regular expressions is a sure way to induce headaches and eyeball-bleeding.

Edit Distance

The edit distance (aka Levenshtein distance) is the number of single character edits it would take to transform one string into another. Thefore, the smaller the edit distance, the more similar two strings are.

If you want to do edit distance calculations, checkout the standalone editdist module. Its distance function takes 2 strings and returns the Levenshtein edit distance. It's also implemented in C, and so is quite fast.

Fuzzywuzzy

Fuzzywuzzy is a great all-purpose library for fuzzy string matching, built (in part) on top of Python's difflib. It has a number of different fuzzy matching functions, and it's definitely worth experimenting with all of them. I've personally found ratio and token_set_ratio to be the most useful.

NLTK

If you want to do some custom fuzzy string matching, then NLTK is a great library to use. There's word tokenizers, stemmers, and it even has its own edit distance implementation. Here's a way you could combine all 3 to create a fuzzy string matching function.

from nltk import metrics, stem, tokenize

stemmer = stem.PorterStemmer()

def normalize(s):
	words = tokenize.wordpunct_tokenize(s.lower().strip())
	return ' '.join([stemmer.stem(w) for w in words])

def fuzzy_match(s1, s2, max_dist=3):
	return metrics.edit_distance(normalize(s1), normalize(s2)) <= max_dist

Phonetics

Finally, an interesting and perhaps non-obvious way to compare strings is with phonetic algorithms. The idea is that 2 strings that sound same may be the same (or at least similar enough). One of the most well known phonetic algorithms is Soundex, with a python soundex algorithm here. Another is Double Metaphone, with a python metaphone module here. You can also find code for these and other phonetic algorithms in the nltk-trainer phonetics module (copied from a now defunct sourceforge project called advas). Using any of these algorithms, you get an encoded string, and then if 2 encodings compare equal, the original strings match. Theoretically, you could even do fuzzy matching on the phonetic encodings, but that's probably pushing the bounds of fuzziness a bit too far.

• tokenization and why it's not as easy as str.split()
• part-of-speech tagging and why it's important
• chunking and named entity recognition
• text classification and how it works for sentiment analysis
• training your own models with nltk-trainer

I'll also be soliciting feedback for a NLTK Tutorial at PyCON 2012. So if you'll be at the meetup and are interested in attending a NLTK tutorial, come find me and tell me what you'd want to learn.

Updated 9/15/2011: Slides from the talk are online - NLTK in 20 minutes

1. There's almost as much jQuery & Javascript as there is Python.
2. Most of the Python code is not Python 3 specific, and the code that is could easily be translate to Python 2.
3. Much of the Python code either uses CherryPy or is for generating HTML. This is not immediately obvious, but becomes apparent in Chapter 3 (which is available as a free PDF download: Chapter 3 - Tasklist I Persistence).

Second, this book is also supposed to be a Beginner's Guide, but that is definitely not the case. To really grasp what's going on, you need to already know the basics of HTML, jQuery interaction, and how HTTP works. Chapter 1 is an excellent introduction to HTTP and web application development, but the book as a whole is not beginner material. I think that anything that uses Python metaclasses automatically becomes at least intermediate level, if not expert, and the main thrust of Chapter 7 is refactoring all your straightforward database code to use complicated metaclasses.

However, if you mentally rewrite the title to be "Web Framework Development from scratch using CherryPy and jQuery", then you've got the right idea. The book steps you through web app development with CherryPy, database models with sqlite3, and plenty of HTML and jQuery for interface generation and interaction. While creating example applications, you slowly build up a re-usable framework. It's an interesting approach, but unfortunately it gets muddied up with inline HTML rendering. I never thought a language as simple and elegant as Python could be reduced to the ugliness of common PHP, but generating HTML with string interpolation inside the same functions that are accessing the database gets pretty close. I kept expecting the author to introduce template rendering, which is a major part of most modern web development frameworks, but it never happened, despite the plethora of excellent Python templating libraries.

While reading this book, I often had the recurring thought "I'm so glad I use Django". If your aim is rapid application development, this is not the book for you. However, if you're interested in creating your own web development framework, or would at least like to understand how a framework like Django could be created, then buy a copy Python 3 Web Development.

• part-of-speech tagging & chunking
• text classification
• creating a custom corpus and corpus reader
• training custom models (manually and/or with nltk-trainer)
• bootstrapping a custom corpus for text classification

Or I could do a high-level survey of many NLTK modules and corpora. Please let me know what you think in the comments, if you plan on going to PyCon 2012, and if you'd want to attend a tutorial on NLTK. You can also contact me directly if you prefer.

Co-Hosting

If you've done this kind of thing before, have some teaching and/or speaking experience, and you feel you could add value (maybe you're a computational linguist or NLP'er and/or have used NLTK professionally), I'd be happy to work with a co-host. Contact me if you're interested, or leave a note in the comments.

Enter roundup by Blake Mizerany. (NOTE: do not try to do apt-get install roundup. You will get an issue tracking system, not a script testing tool).

Roundup provides a great way to prevent shell bugs by creating simple test functions within a shell script. Here's the first dozen lines of train_classifier.sh, which you can probably guess tests train_classifier.py:

#!/usr/bin/env roundup

describe "train_classifier.py"

it_displays_usage_when_no_arguments() {
	./train_classifier.py 2>&1 | grep -q "usage: train_classifier.py"
}

it_cannot_find_foo() {
	last_line=$(./train_classifier.py foo 2>&1 | tail -n 1)
	test "$last_line" "=" "ValueError: cannot find corpus path for foo"
}

describe is like the name of a module or test case, and all test functions begin with test_. Within the test functions, you use standard shell commands that should produce no output on success (like grep -q or the test command). You can also match multiple lines of output, as in:

it_trains_movie_reviews_paras() {
	test "$(./train_classifier.py movie_reviews --no-pickle --no-eval --fraction 0.5 --instances paras)" "=" "loading movie_reviews
2 labels: ['neg', 'pos']
1000 training feats, 1000 testing feats
training NaiveBayes classifier"
}

Once you've got all your test functions defined, make sure your test script is executable and roundup is installed, then run your test script. You'll get nice output that looks like:

nltk-trainer$ tests/train_classifier.sh
train_classifier.py
  it_displays_usage_when_no_arguments:             [PASS]
  it_cannot_find_foo:                              [PASS]
  it_cannot_import_reader:                         [PASS]
  it_trains_movie_reviews_paras:                   [PASS]
  it_trains_corpora_movie_reviews_paras:           [PASS]
  it_cross_fold_validates:                         [PASS]
  it_trains_movie_reviews_sents:                   [PASS]
  it_trains_movie_reviews_maxent:                  [PASS]
  it_shows_most_informative:                       [PASS]
=========================================================
Tests:    9 | Passed:   9 | Failed:   0

So far, roundup has been a perfect tool for testing all the nltk-trainer scripts, and the only downside is the one-time manual installation. I highly recommend it for anyone writing custom commands and scripts, no matter what language you use to write them.

1. Basic introduction to testing with unittest, which is great if you're just starting with Python and testing.
2. Good coverage of nose. I was pleasantly surprised at how easy it is to write nose plugins.
3. Deep coverage of using doctest and writing testable docstrings. You can download a free PDF of Chapter 3 here.
4. BDD with a cool nose plugin, and how to use mock or mockito for testing with mock objects. I wish the author had expressed an opinion in favor of either mock or mockito, but he didn't, so I will: use Fudge. Chapter 4 also covers the Lettuce DSL, which I think is pretty neat, but since every step requires writing a function handler, I'm not convinced it's actually easier or better than writing doctests or unit tests.
5. Acceptance testing with Pyccuracy and Robot Framework, which both give you a way to use Selenium from Python. I thought the DSLs used seemed too "magic", but I that's probably because I didn't know the command words, and they weren't highlighted or adequately explained.
6. How to install and use Jenkins and TeamCity, and how to display XML reports produced using NoseXUnit. This is a very useful chapter for anyone thinking about or setting up continuous integration.
7. This chapter is supposed to be about test coverage, and does introduce coverage, but the examples get needlessly complicated. Previous chapters used a simple shopping cart example, but this chapter uses network events, which really distracts from the tests. The author also writes unittests that just print the results intead of actually testing results with assertions.
8. More network event complexity while trying to demonstrate smoke testing and load testing. This chapter would have made a lot more sense in a book about network programming and how to test network events. Pyro is used with very little explanation, and MySQL and SQLlite are brought in too, increasing the complexity even more.
9. This chapter is filled with useful advice, but there's no actual code examples. Instead, the advice is shoehorned into the cookbook format, which I felt detracted from the otherwise great content.

Throughout the book, the author presents a kind of "main script" that he updates at the end of many of the chapters. However, the script also contains stub functions that are never touched and barely explained, making their existance completely unnecessary. There's also far too many import *, which can make it difficult to understand the code. But I did learn enough new things that I think Python Testing Cookbook is worth buying and reading. Leaving out Chapters 7 and 8, I think the book is a great resource if you're just getting started with testing, you want to do continuous integration, and/or you want to get non-programmers involved in the testing process. There's also a blog about the book, which has links to other reviews.

Programming Collective Intelligence is a great conceptual introduction to many common machine learning algorithms and techniques. It covers classification algorithms such as Naive Bayes and Neural Networks, and algorithmic optimization approaches like Genetic Programming. The book also manages to pick interesting example applications, such as stock price prediction and topic identification.

There are two chapters in particular that stand out to me. First is Chapter 6, which covers Naive Bayes classification. What stood out was that the algorithm presented is an online learner, which means it can be updated as data comes in, unlike the NLTK NaiveBayesClassifier, which can be trained only once. Another thing that caught my attention was Fisher's method, which is not implemented in NLTK, but could be with a little work. Apparently Fisher's method is great for spam filtering, and is used by the SpamBayes Outlook plugin (which is also written in Python).

Second, I found Chapter 9, which covers Support Vector Machines and Kernel Methods, to be quite intuitive. It explains the idea by starting with examples of linear classification and its shortfalls. But then the examples show that by scaling the data in a particular way first, linear classification suddenly becomes possible. And the kernel trick is simply a neat and efficient way to reduce the amount of calculation necessary to train a classifier on scaled data.

The final chapter summarizes all the key algorithms, and for many it includes commentary on their strengths and weaknesses. This seems like valuable reference material, especially for when you have a new data set to learn from, and you're not sure which algorithms will help get the results you're looking for. Overall, I found Programming Collective Intelligence to be an enjoyable read on my Kindle 3, and highly recommend it to anyone getting started with machine learning and Python, as well as anyone interested in a general survey of machine learning algorithms.

Update on 07/10/2011: slides are online from my talk: NLTK: the Good, the Bad, and the Awesome.

I recently finished reading Programming Collective Intellegince and will be posting a review soon. The TL;DR review is: get it if want an great introduction to machine learning with Python. It covers a lot of complex algorithms in a simple way, and provides some great example use cases.

Python Testing Cookbook

Testing is something nearly every developer can do more of, and this Python Testing Cookbook looks to be full of techniques for integrating testing at various levels of a project. As a preview, you can download a PDF of Chapter 3 - Creating Testable Documentation with doctest.

Python 3 Web Development Beginner's Guide

I haven't used Python 3 yet, so Python 3 Web Development Beginner's Guide is a good excuse to do so. I also haven't done any web development outside of Django in a few years, and I'm interested to see how it compares to doing it from scratch. As a preview, you can download a PDF of Chapter 3 - Tasklist I Persistence.

Kindle 3

I'm reading all of these on a Kindle 3, which has worked out surprisingly well. It's obviously not good for copy & pasting code snippets, but that's generally a bad idea anyway. And if don't want to type code in yourself, you can always download it from the publisher's site.