README.md in omnicat-0.1.3 vs README.md in omnicat-0.2.0
- old
+ new
@@ -1,10 +1,10 @@
# OmniCat
[](https://travis-ci.org/mustafaturan/omnicat) [](https://codeclimate.com/github/mustafaturan/omnicat)
-A generalized framework for text classifications. For now, it only supports Naive Bayes algorithm for text classification.
+A generalized framework for text classifications.
## Installation
Add this line to your application's Gemfile:
@@ -18,79 +18,35 @@
$ gem install omnicat
## Usage
-See rdoc for detailed usage.
+Stand-alone version of omnicat is just a strategy holder for developers. Its aim is providing omnification of methods for text classification gems with loseless conversion of a strategy to another one. End-users should see 'classifier strategies' section and 'changing classifier strategy' sub section.
-### Bayes classifier
-Create a Bayes classifier object.
+### Changing classifier strategy
- bayes = OmniCat::Classifiers::Bayes.new
+OmniCat allows you to change strategy on runtime.
-### Create categories
-Create a classification category.
+ # Declare classifier with Naive Bayes classifier
+ classifier = OmniCat::Classifier.new(OmniCat::Classifiers::Bayes.new())
+ ...
+ # do some operations like adding category, training, etc...
+ ...
+ # make some classification using Bayes
+ classifier.classify('I am happy :)')
+ ...
+ # change strategy to Support Vector Machine (SVM) on runtime
+ classifier = OmniCat::Classifier.new(OmniCat::Classifiers::SVM.new())
+ # now you do not need to re-train, add category and so on..
+ # just classify with new strategy
+ classifier.classify('I am happy :)')
- bayes.add_category('positive')
- bayes.add_category('negative')
+## Classifier strategies
+Here is the classifier list avaliable for OmniCat.
-### 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...
+### Naive Bayes classifier
+* gem 'omnicat-bayes'
+* Details: http://github.com/mustafaturan/omnicat-bayes
## Contributing
1. Fork it
2. Create your feature branch (`git checkout -b my-new-feature`)