Class: Linguist::Classifier

Inherits:
Object
  • Object
show all
Defined in:
lib/linguist/classifier.rb

Overview

Language bayesian classifier.

Constant Summary collapse

CLASSIFIER_CONSIDER_BYTES = 
50 * 1024

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(db = {}) ⇒ Classifier

Internal: Initialize a Classifier.



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# File 'lib/linguist/classifier.rb', line 84

def initialize(db = {})
  @tokens_total    = db['tokens_total']
  @languages_total = db['languages_total']
  @tokens          = db['tokens']
  @language_tokens = db['language_tokens']
  @languages       = db['languages']
end

Class Method Details

.call(blob, possible_languages) ⇒ Object

Public: Use the classifier to detect language of the blob.

blob - An object that quacks like a blob. possible_languages - Array of Language objects

Examples

Classifier.call(FileBlob.new("path/to/file"), [
  Language["Ruby"], Language["Python"]
])

Returns an Array of Language objects, most probable first.



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# File 'lib/linguist/classifier.rb', line 20

def self.call(blob, possible_languages)
  language_names = possible_languages.map(&:name)
  classify(Samples.cache, blob.data[0...CLASSIFIER_CONSIDER_BYTES], language_names).map do |name, _|
    Language[name] # Return the actual Language objects
  end
end

.classify(db, tokens, languages = nil) ⇒ Object

Public: Guess language of data.

db - Hash of classifier tokens database. data - Array of tokens or String data to analyze. languages - Array of language name Strings to restrict to.

Examples

Classifier.classify(db, "def hello; end")
# => [ 'Ruby', 0.90], ['Python', 0.2], ... ]

Returns sorted Array of result pairs. Each pair contains the String language name and a Float score.



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# File 'lib/linguist/classifier.rb', line 78

def self.classify(db, tokens, languages = nil)
  languages ||= db['languages'].keys
  new(db).classify(tokens, languages)
end

.train!(db, language, data) ⇒ Object

Public: Train classifier that data is a certain language.

db - Hash classifier database object language - String language of data data - String contents of file

Examples

Classifier.train(db, 'Ruby', "def hello; end")

Returns nothing.

Set LINGUIST_DEBUG=1 or =2 to see probabilities per-token or per-language. See also #dump_all_tokens, below.



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# File 'lib/linguist/classifier.rb', line 41

def self.train!(db, language, data)
  tokens = Tokenizer.tokenize(data)

  db['tokens_total'] ||= 0
  db['languages_total'] ||= 0
  db['tokens'] ||= {}
  db['language_tokens'] ||= {}
  db['languages'] ||= {}

  tokens.each do |token|
    db['tokens'][language] ||= {}
    db['tokens'][language][token] ||= 0
    db['tokens'][language][token] += 1
    db['language_tokens'][language] ||= 0
    db['language_tokens'][language] += 1
    db['tokens_total'] += 1
  end
  db['languages'][language] ||= 0
  db['languages'][language] += 1
  db['languages_total'] += 1

  nil
end

Instance Method Details

#classify(tokens, languages) ⇒ Object

Internal: Guess language of data

data - Array of tokens or String data to analyze. languages - Array of language name Strings to restrict to.

Returns sorted Array of result pairs. Each pair contains the String language name and a Float score.



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# File 'lib/linguist/classifier.rb', line 99

def classify(tokens, languages)
  return [] if tokens.nil? || languages.empty?
  tokens = Tokenizer.tokenize(tokens) if tokens.is_a?(String)
  scores = {}

  debug_dump_all_tokens(tokens, languages) if verbosity >= 2

  languages.each do |language|
    scores[language] = tokens_probability(tokens, language) + language_probability(language)
    debug_dump_probabilities(tokens, language, scores[language]) if verbosity >= 1
  end

  scores.sort { |a, b| b[1] <=> a[1] }.map { |score| [score[0], score[1]] }
end

#language_probability(language) ⇒ Object

Internal: Probably of a language occurring - P©

language - Language to check.

Returns Float between 0.0 and 1.0.



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# File 'lib/linguist/classifier.rb', line 145

def language_probability(language)
  Math.log(@languages[language].to_f / @languages_total.to_f)
end

#token_probability(token, language) ⇒ Object

Internal: Probably of token in language occurring - P(F | C)

token - String token. language - Language to check.

Returns Float between 0.0 and 1.0.



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# File 'lib/linguist/classifier.rb', line 132

def token_probability(token, language)
  if @tokens[language][token].to_f == 0.0
    1 / @tokens_total.to_f
  else
    @tokens[language][token].to_f / @language_tokens[language].to_f
  end
end

#tokens_probability(tokens, language) ⇒ Object

Internal: Probably of set of tokens in a language occurring - P(D | C)

tokens - Array of String tokens. language - Language to check.

Returns Float between 0.0 and 1.0.



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# File 'lib/linguist/classifier.rb', line 120

def tokens_probability(tokens, language)
  tokens.inject(0.0) do |sum, token|
    sum += Math.log(token_probability(token, language))
  end
end