Skip to content

Latest commit

 

History

History
162 lines (124 loc) · 4.86 KB

README.md

File metadata and controls

162 lines (124 loc) · 4.86 KB
Raw

stuff-classifier

A library for classifying text into multiple categories.

Currently provided classifiers:

Ran a benchmark of 1345 items that I have previously manually classified with multiple categories. Here's the rate over which the 2 algorithms have correctly detected one of those categories:

  • Bayes: 79.26%
  • Tf-Idf: 81.34%

I prefer the Naive Bayes approach, because while having lower stats on this benchmark, it seems to make better decisions than I did in many cases. For example, an item with title "Paintball Session, 100 Balls and Equipment" was classified as "Activities" by me, but the bayes classifier identified it as "Sports", at which point I had an intellectual orgasm. Also, the Tf-Idf classifier seems to do better on clear-cut cases, but doesn't seem to handle uncertainty so well. Of course, these are just quick tests I made and I have no idea which is really better.

Install

gem install stuff-classifier

Usage

You either instantiate one class or the other. Both have the same signature:

require 'stuff-classifier'

# for the naive bayes implementation
cls = StuffClassifier::Bayes.new("Cats or Dogs")

# for the Tf-Idf based implementation
cls = StuffClassifier::TfIdf.new("Cats or Dogs")

# these classifiers use word stemming by default, but if it has weird
# behavior, then you can disable it on init:
cls = StuffClassifier::TfIdf.new("Cats or Dogs", :stemming => false)

# also by default, the parsing phase filters out stop words, to
# disable or to come up with your own list of stop words, on a
# classifier instance you can do this:
cls.ignore_words = [ 'the', 'my', 'i', 'dont' ]

Training the classifier:

cls.train(:dog, "Dogs are awesome, cats too. I love my dog")
cls.train(:cat, "Cats are more preferred by software developers. I never could stand cats. I have a dog")    
cls.train(:dog, "My dog's name is Willy. He likes to play with my wife's cat all day long. I love dogs")
cls.train(:cat, "Cats are difficult animals, unlike dogs, really annoying, I hate them all")
cls.train(:dog, "So which one should you choose? A dog, definitely.")
cls.train(:cat, "The favorite food for cats is bird meat, although mice are good, but birds are a delicacy")
cls.train(:dog, "A dog will eat anything, including birds or whatever meat")
cls.train(:cat, "My cat's favorite place to purr is on my keyboard")
cls.train(:dog, "My dog's favorite place to take a leak is the tree in front of our house")

And finally, classifying stuff:

cls.classify("This test is about cats.")
#=> :cat
cls.classify("I hate ...")
#=> :cat
cls.classify("The most annoying animal on earth.")
#=> :cat
cls.classify("The preferred company of software developers.")
#=> :cat
cls.classify("My precious, my favorite!")
#=> :cat
cls.classify("Get off my keyboard!")
#=> :cat
cls.classify("Kill that bird!")
#=> :cat

cls.classify("This test is about dogs.")
#=> :dog
cls.classify("Cats or Dogs?") 
#=> :dog
cls.classify("What pet will I love more?")    
#=> :dog
cls.classify("Willy, where the heck are you?")
#=> :dog
cls.classify("I like big buts and I cannot lie.") 
#=> :dog
cls.classify("Why is the front door of our house open?")
#=> :dog
cls.classify("Who is eating my meat?")
#=> :dog

Persistency

The following layers for saving the training data between sessions are implemented:

  • in memory (by default)
  • on disk
  • Redis
  • (coming soon) in a RDBMS

To persist the data in Redis, you can do this:

# defaults to redis running on localhost on default port
store = StuffClassifier::RedisStorage.new(@key)

# pass in connection args
store = StuffClassifier::RedisStorage.new(@key, {host:'my.redis.server.com', port: 4829})

To persist the data on disk, you can do this:

store = StuffClassifier::FileStorage.new(@storage_path)

# global setting
StuffClassifier::Base.storage = store

# or alternative local setting on instantiation, by means of an
# optional param ...
cls = StuffClassifier::Bayes.new("Cats or Dogs", :storage => store)

# after training is done, to persist the data ...
cls.save_state

# or you could just do this:
StuffClassifier::Bayes.open("Cats or Dogs") do |cls|
  # when done, save_state is called on END
end

# to start fresh, deleting the saved training data for this classifier
StuffClassifier::Bayes.new("Cats or Dogs", :purge_state => true)

The name you give your classifier is important, as based on it the data will get loaded and saved. For instance, following 3 classifiers will be stored in different buckets, being independent of each other.

cls1 = StuffClassifier::Bayes.new("Cats or Dogs")
cls2 = StuffClassifier::Bayes.new("True or False")
cls3 = StuffClassifier::Bayes.new("Spam or Ham")

License

MIT Licensed. See LICENSE.txt for details.