Class: Ebooks::Model
- Inherits:
-
Object
- Object
- Ebooks::Model
- Defined in:
- lib/twitter_ebooks/model.rb
Instance Attribute Summary collapse
-
#keywords ⇒ Array<String>
The top 200 most important keywords, in descending order.
-
#mentions ⇒ Array<Array<Integer>>
Sentences derived from Twitter mentions.
-
#sentences ⇒ Array<Array<Integer>>
Sentences represented by arrays of tikis.
-
#tokens ⇒ Array<String>
An array of unique tokens.
Class Method Summary collapse
-
.consume(path) ⇒ Ebooks::Model
Generate a new model from a corpus file.
-
.consume_all(paths) ⇒ Ebooks::Model
Generate a new model from multiple corpus files.
-
.load(path) ⇒ Ebooks::Model
Load a saved model.
Instance Method Summary collapse
-
#append(path) ⇒ Object
Append a generated model to existing model file instead of overwriting it.
-
#consume(path) ⇒ Object
Consume a corpus into this model.
-
#consume_all(paths) ⇒ Object
Consume multiple corpuses into this model.
-
#consume_lines(lines) ⇒ Object
Consume a sequence of lines.
-
#find_relevant(sentences, input) ⇒ Array<Array<Array<Integer>>, Array<Array<Integer>>>
Finds relevant and slightly relevant tokenized sentences to input comparing non-stopword token overlaps.
-
#fix(text) ⇒ String
Correct encoding issues in generated text.
-
#initialize ⇒ Model
constructor
A new instance of Model.
-
#make_response(input, limit = 280, sentences = @mentions) ⇒ String
Generates a response by looking for related sentences in the corpus and building a smaller generator from these.
-
#make_statement(limit = 280, generator = nil, retry_limit = 10) ⇒ String
Generate some text.
-
#mass_tikify(text) ⇒ Array<Array<Integer>>
Convert a body of text into arrays of tikis.
-
#save(path) ⇒ Object
Save model to a file.
-
#tikify(token) ⇒ Integer
Reverse lookup a token index from a token.
-
#valid_tweet?(tikis, limit) ⇒ Boolean
Check if an array of tikis comprises a valid tweet.
-
#verbatim?(tikis) ⇒ Boolean
Test if a sentence has been copied verbatim from original.
Constructor Details
#initialize ⇒ Model
Returns a new instance of Model.
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# File 'lib/twitter_ebooks/model.rb', line 101 def initialize @tokens = [] # Reverse lookup tiki by token, for faster generation @tikis = {} end |
Instance Attribute Details
#keywords ⇒ Array<String>
The top 200 most important keywords, in descending order
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# File 'lib/twitter_ebooks/model.rb', line 27 def keywords @keywords end |
#mentions ⇒ Array<Array<Integer>>
Sentences derived from Twitter mentions
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# File 'lib/twitter_ebooks/model.rb', line 23 def mentions @mentions end |
#sentences ⇒ Array<Array<Integer>>
Sentences represented by arrays of tikis
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# File 'lib/twitter_ebooks/model.rb', line 19 def sentences @sentences end |
#tokens ⇒ Array<String>
An array of unique tokens. This is the main source of actual strings in the model. Manipulation of a token is done using its index in this array, which we call a “tiki”
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# File 'lib/twitter_ebooks/model.rb', line 15 def tokens @tokens end |
Class Method Details
.consume(path) ⇒ Ebooks::Model
Generate a new model from a corpus file
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# File 'lib/twitter_ebooks/model.rb', line 32 def self.consume(path) Model.new.consume(path) end |
.consume_all(paths) ⇒ Ebooks::Model
Generate a new model from multiple corpus files
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# File 'lib/twitter_ebooks/model.rb', line 39 def self.consume_all(paths) Model.new.consume_all(paths) end |
.load(path) ⇒ Ebooks::Model
Load a saved model
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# File 'lib/twitter_ebooks/model.rb', line 46 def self.load(path) model = Model.new model.instance_eval do props = Marshal.load(File.open(path, 'rb') { |f| f.read }) @tokens = props[:tokens] @sentences = props[:sentences] @mentions = props[:mentions] @keywords = props[:keywords] end model end |
Instance Method Details
#append(path) ⇒ Object
Append a generated model to existing model file instead of overwriting it
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# File 'lib/twitter_ebooks/model.rb', line 74 def append(path) existing = File.file?(path) if !existing log "No existing model found at #{path}" return else #read-in and deserialize existing model props = Marshal.load(File.open(path,'rb') { |old| old.read }) old_tokens = props[:tokens] old_sentences = props[:sentences] old_mentions = props[:mentions] old_keywords = props[:keywords] #append existing properties to new ones and overwrite with new model File.open(path, 'wb') do |f| f.write(Marshal.dump({ tokens: @tokens.concat(old_tokens), sentences: @sentences.concat(old_sentences), mentions: @mentions.concat(old_mentions), keywords: @keywords.concat(old_keywords) })) end end self end |
#consume(path) ⇒ Object
Consume a corpus into this model
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# File 'lib/twitter_ebooks/model.rb', line 139 def consume(path) content = File.read(path, :encoding => 'utf-8') if path.split('.')[-1] == "json" log "Reading json corpus from #{path}" lines = JSON.parse(content).map do |tweet| tweet['text'] || tweet['full_text'] end elsif path.split('.')[-1] == "csv" log "Reading CSV corpus from #{path}" content = CSV.parse(content) header = content.shift text_col = header.index('text') lines = content.map do |tweet| tweet[text_col] end else log "Reading plaintext corpus from #{path} (if this is a json or csv file, please rename the file with an extension and reconsume)" lines = content.split("\n") end consume_lines(lines) end |
#consume_all(paths) ⇒ Object
Consume multiple corpuses into this model
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# File 'lib/twitter_ebooks/model.rb', line 200 def consume_all(paths) lines = [] paths.each do |path| content = File.read(path, :encoding => 'utf-8') if path.split('.')[-1] == "json" log "Reading json corpus from #{path}" l = JSON.parse(content).map do |tweet| tweet['text'] || tweet['full_text'] end lines.concat(l) elsif path.split('.')[-1] == "csv" log "Reading CSV corpus from #{path}" content = CSV.parse(content) header = content.shift text_col = header.index('text') l = content.map do |tweet| tweet[text_col] end lines.concat(l) else log "Reading plaintext corpus from #{path}" l = content.split("\n") lines.concat(l) end end consume_lines(lines) end |
#consume_lines(lines) ⇒ Object
Consume a sequence of lines
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# File 'lib/twitter_ebooks/model.rb', line 165 def consume_lines(lines) log "Removing commented lines and sorting mentions" statements = [] mentions = [] lines.each do |l| next if l.start_with?('#') # Remove commented lines next if l.include?('RT') || l.include?('MT') # Remove soft retweets if l.include?('@') mentions << NLP.normalize(l) else statements << NLP.normalize(l) end end text = statements.join("\n").encode('UTF-8', :invalid => :replace) mention_text = mentions.join("\n").encode('UTF-8', :invalid => :replace) lines = nil; statements = nil; mentions = nil # Allow garbage collection log "Tokenizing #{text.count("\n")} statements and #{mention_text.count("\n")} mentions" @sentences = mass_tikify(text) @mentions = mass_tikify(mention_text) log "Ranking keywords" @keywords = NLP.keywords(text).top(200).map(&:to_s) log "Top keywords: #{@keywords[0]} #{@keywords[1]} #{@keywords[2]}" self end |
#find_relevant(sentences, input) ⇒ Array<Array<Array<Integer>>, Array<Array<Integer>>>
Finds relevant and slightly relevant tokenized sentences to input comparing non-stopword token overlaps
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# File 'lib/twitter_ebooks/model.rb', line 295 def find_relevant(sentences, input) relevant = [] slightly_relevant = [] tokenized = NLP.tokenize(input).map(&:downcase) sentences.each do |sent| tokenized.each do |token| if sent.map { |tiki| @tokens[tiki].downcase }.include?(token) relevant << sent unless NLP.stopword?(token) slightly_relevant << sent end end end [relevant, slightly_relevant] end |
#fix(text) ⇒ String
Correct encoding issues in generated text
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# File 'lib/twitter_ebooks/model.rb', line 232 def fix(text) NLP.htmlentities.decode text end |
#make_response(input, limit = 280, sentences = @mentions) ⇒ String
Generates a response by looking for related sentences in the corpus and building a smaller generator from these
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# File 'lib/twitter_ebooks/model.rb', line 319 def make_response(input, limit=280, sentences=@mentions) # Prefer mentions relevant, slightly_relevant = find_relevant(sentences, input) if relevant.length >= 3 generator = SuffixGenerator.build(relevant) make_statement(limit, generator) elsif slightly_relevant.length >= 5 generator = SuffixGenerator.build(slightly_relevant) make_statement(limit, generator) elsif sentences.equal?(@mentions) make_response(input, limit, @sentences) else make_statement(limit) end end |
#make_statement(limit = 280, generator = nil, retry_limit = 10) ⇒ String
Generate some text
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# File 'lib/twitter_ebooks/model.rb', line 249 def make_statement(limit=280, generator=nil, retry_limit=10) responding = !generator.nil? generator ||= SuffixGenerator.build(@sentences) retries = 0 tweet = "" while (tikis = generator.generate(3, :bigrams)) do #log "Attempting to produce tweet try #{retries+1}/#{retry_limit}" break if (tikis.length > 3 || responding) && valid_tweet?(tikis, limit) retries += 1 break if retries >= retry_limit end if verbatim?(tikis) && tikis.length > 3 # We made a verbatim tweet by accident #log "Attempting to produce unigram tweet try #{retries+1}/#{retry_limit}" while (tikis = generator.generate(3, :unigrams)) do break if valid_tweet?(tikis, limit) && !verbatim?(tikis) retries += 1 break if retries >= retry_limit end end tweet = NLP.reconstruct(tikis, @tokens) if retries >= retry_limit log "Unable to produce valid non-verbatim tweet; using \"#{tweet}\"" end fix tweet end |
#mass_tikify(text) ⇒ Array<Array<Integer>>
Convert a body of text into arrays of tikis
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# File 'lib/twitter_ebooks/model.rb', line 124 def mass_tikify(text) sentences = NLP.sentences(text) sentences.map do |s| tokens = NLP.tokenize(s).reject do |t| # Don't include usernames/urls as tokens t.include?('@') || t.include?('http') end tokens.map { |t| tikify(t) } end end |
#save(path) ⇒ Object
Save model to a file
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# File 'lib/twitter_ebooks/model.rb', line 60 def save(path) File.open(path, 'wb') do |f| f.write(Marshal.dump({ tokens: @tokens, sentences: @sentences, mentions: @mentions, keywords: @keywords })) end self end |
#tikify(token) ⇒ Integer
Reverse lookup a token index from a token
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# File 'lib/twitter_ebooks/model.rb', line 111 def tikify(token) if @tikis.has_key?(token) then return @tikis[token] else (@tokens.length+1)%1000 == 0 and puts "#{@tokens.length+1} tokens" @tokens << token return @tikis[token] = @tokens.length-1 end end |
#valid_tweet?(tikis, limit) ⇒ Boolean
Check if an array of tikis comprises a valid tweet
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# File 'lib/twitter_ebooks/model.rb', line 239 def valid_tweet?(tikis, limit) tweet = NLP.reconstruct(tikis, @tokens) tweet.length <= limit && !NLP.unmatched_enclosers?(tweet) end |
#verbatim?(tikis) ⇒ Boolean
Test if a sentence has been copied verbatim from original
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# File 'lib/twitter_ebooks/model.rb', line 286 def verbatim?(tikis) @sentences.include?(tikis) || @mentions.include?(tikis) end |