OmniCat

A generalized framework for text classifications. For now, it only supports Naive Bayes algorithm for text classification.

Installation

Add this line to your application's Gemfile:

gem 'omnicat'

And then execute:

$ bundle

Or install it yourself as:

$ gem install omnicat

Usage

See rdoc for detailed usage.

Bayes classifier

Create a Bayes classifier object.

bayes = OmniCat::Classifiers::Bayes.new

Create categories

Create a classification category.

bayes.add_category('positive')
bayes.add_category('negative')

Train

Train category with a document.

bayes.train('positive', 'great if you are in a slap happy mood .')
bayes.train('negative', 'bad tracking issue')

Train batch

Train category with multiple documents.

bayes.train_batch('positive', [
  'a feel-good picture in the best sense of the term...',
  'it is a feel-good movie about which you can actually feel good.',
  'love and money both of them are good choises'
])
bayes.train_batch('negative', [
  'simplistic , silly and tedious .',
  'interesting , but not compelling . ',
  'seems clever but not especially compelling'
])

Classify

Classify a document.

result = bayes.classify('I feel so good and happy')
=> #<OmniCat::Result:0x007fd20296aad8 @category={:name=>"positive", :percentage=>73}, @scores={"positive"=>5.4253472222222225e-09, "negative"=>1.9600796074572086e-09}, @total_score=7.385426829679431e-09>
result.to_hash
=> {:category=>{:name=>"positive", :percentage=>73}, :scores=>{"positive"=>5.4253472222222225e-09, "negative"=>1.9600796074572086e-09}, :total_score=>7.385426829679431e-09}

Classify batch

Classify multiple documents at a time.

results = bayes.classify_batch(
  [
    'the movie is silly so not compelling enough',
    'a good piece of work'
  ]
)
=> [#<OmniCat::Result:0x007fd2029341b8 @category={:name=>"negative", :percentage=>78}, @scores={"positive"=>2.5521869888765736e-14, "negative"=>9.074442627116706e-14}, @total_score=1.162662961599328e-13>, #<OmniCat::Result:0x007fd20292e7e0 @category={:name=>"positive", :percentage=>80}, @scores={"positive"=>2.411265432098765e-07, "negative"=>5.880238822371627e-08}, @total_score=2.999289314335928e-07>]

Convert to hash

Convert full Bayes object to hash.

# For storing, restoring modal data
bayes_hash = bayes.to_hash
=> {:categories=>{"positive"=>{:doc_count=>4, :tokens=>{"great"=>1, "if"=>1, "you"=>2, "are"=>2, "in"=>2, "slap"=>1, "happy"=>1, "mood"=>1, "feel-good"=>2, "picture"=>1, "the"=>2, "best"=>1, "sense"=>1, "of"=>2, "term"=>1, "it"=>1, "is"=>1, "movie"=>1, "about"=>1, "which"=>1, "can"=>1, "actually"=>1, "feel"=>1, "good"=>2, "love"=>1, "and"=>1, "money"=>1, "both"=>1, "them"=>1, "choises"=>1}, :token_count=>37}, "negative"=>{:doc_count=>4, :tokens=>{"bad"=>1, "tracking"=>1, "issue"=>1, "simplistic"=>1, "silly"=>1, "and"=>1, "tedious"=>1, "interesting"=>1, "but"=>2, "not"=>2, "compelling"=>2, "seems"=>1, "clever"=>1, "especially"=>1}, :token_count=>17}}, :category_count=>2, :doc_count=>8, :k_value=>1.0, :token_count=>54, :uniq_token_count=>43}

Load from hash

Load full Bayes object from hash.

another_bayes_obj = OmniCat::Classifiers::Bayes.new(bayes_hash)
=> #<OmniCat::Classifiers::Bayes:0x007fd20308cff0 @categories={"positive"=>#<OmniCat::Classifiers::BayesInternals::Category:0x007fd20308cf78 @doc_count=4, @tokens={"great"=>1, "if"=>1, "you"=>2, "are"=>2, "in"=>2, "slap"=>1, "happy"=>1, "mood"=>1, "feel-good"=>2, "picture"=>1, "the"=>2, "best"=>1, "sense"=>1, "of"=>2, "term"=>1, "it"=>1, "is"=>1, "movie"=>1, "about"=>1, "which"=>1, "can"=>1, "actually"=>1, "feel"=>1, "good"=>2, "love"=>1, "and"=>1, "money"=>1, "both"=>1, "them"=>1, "choises"=>1}, @token_count=37>, "negative"=>#<OmniCat::Classifiers::BayesInternals::Category:0x007fd20308cf00 @doc_count=4, @tokens={"bad"=>1, "tracking"=>1, "issue"=>1, "simplistic"=>1, "silly"=>1, "and"=>1, "tedious"=>1, "interesting"=>1, "but"=>2, "not"=>2, "compelling"=>2, "seems"=>1, "clever"=>1, "especially"=>1}, @token_count=17>}, @category_count=2, @doc_count=8, @k_value=1.0, @token_count=54, @uniq_token_count=43>
another_bayes_obj.classify('best senses')
=> #<OmniCat::Result:0x007fd203075008 @category={:name=>"positive", :percentage=>57}, @scores={"positive"=>0.0002314814814814815, "negative"=>0.00017146776406035664}, @total_score=0.00040294924554183816>

Todo

Add more text classification modules such as Support Vector Machine (SVM).
Add text cleaning/manipulating extensions such as stopwords cleaner, stemmer, and pos-tagger, etc...

Contributing

Fork it
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin my-new-feature)
Create new Pull Request