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#!/usr/bin/env ruby
require 'zipf'
require 'bloom-filter'
class FeatureFactory
def initialize cfg
@use_target_ngrams = false
if cfg['ff_target_ngrams']
@use_target_ngrams = true
args = cfg['ff_target_ngrams'].split
@target_ngrams_n = args[0].to_i
@target_ngrams_fix = true if args.size==2&&args[1]=='fix'
end
@use_phrase_pairs = false
if cfg['ff_phrase_pairs']
@use_phrase_pairs = true
@phrase_table = nil
args = cfg['ff_phrase_pairs'].split
if args.size==2
@phrase_table = BloomFilter.load args.last
end
end
@additional_phrase_pairs = {}
@binary = false
@binary = true if cfg['binary_feature_values']
@filter_features = false
if cfg['filter_features']
@filter_features = true
@stopwords_target = ReadFile.readlines(cfg['filter_features']).map{ |i| i.strip.downcase }
end
end
def produce translation, source
f = SparseVector.new
phrase_pairs(f, translation, source) if @use_phrase_pairs
target_ngrams(f, translation.s) if @use_target_ngrams
return f
end
def filter a
single_char = only_stop = only_num = 1
a.each { |i|
single_char = 0 if i.size > 1
only_stop = 0 if not @stopwords_target.include? i.downcase
only_num = 0 if i.gsub(/[0-9]+/, '').size > 0
}
return [single_char,only_stop,only_num].max==1
end
def phrase_pairs f, translation, source
target_phrases = translation.get_phrases
return if !target_phrases
spans = translation.get_spans
src_tok = source.split.map{ |i| i.strip }
src_sz = 0.0
name = nil
spans.each_with_index { |i,j|
next if @filter_features && filter(target_phrases[j])
i.pop if i.size==2 && i[0]==i[1]
if i.size == 2
next if !src_tok[i[0]..i[1]]
if @phrase_table
pp = "#{src_tok[i[0]..i[1]].join ' '} ||| #{target_phrases[j]}"
next if !(@phrase_table.include?(pp) || @additional_phrase_pairs.has_key?(pp))
end
name = "PP:#{src_tok[i[0]..i[1]].join ','}~#{target_phrases[j].split.join ','}"
src_sz = src_tok[i[0]..i[1]].size.to_f
else
if @phrase_table
pp ="#{src_tok[i[0]]} ||| #{target_phrases[j]}"
next if !(@phrase_table.include?(pp) || @additional_phrase_pairs.has_key?(pp))
end
if i[0] >= 0
name = "PP:#{src_tok[i[0]]}~#{target_phrases[j]}"
src_sz = 1.0
end
end
if @binary
f[name] = 1.0
else
f[name] = src_sz
end
}
end
def add_phrase_pairs pairs
pairs.each { |i| @additional_phrase_pairs[i] = true }
end
def target_ngrams f, s
ngrams(s, @target_ngrams_n, @target_ngrams_fix) { |ng|
next if @filter_features && filter(ng)
name = "NG:"+ng.join("_")
if @binary
f[name] = 1.0
else
f[name] += ng.size
end
}
end
end
class MosesKbestEntryWithPhraseAlignment < Translation
def initialize
super
@scores[:rr] = -1.0/0
end
def get_phrases
@raw.split(/\|-?\d+\||\|\d+-\d+\|/).map{ |i| i.strip }.reject{ |i| i=='' }
end
def _span span
if span == '-1'
return [-1]
else
return span.split('-').map { |i| i.to_i }
end
end
def get_spans
@raw.scan(/\|-?\d+\||\|\d+-\d+\|/).map{ |i| i[1..-2] }.map{ |i| _span i }
end
def score model
@scores[:rr] = model.dot(@f)
end
end
class ConstrainedSearchOracle < MosesKbestEntryWithPhraseAlignment
def from_s s
@id = -1
@raw = s.strip.split(' : ', 2)[1].gsub(/(\[|\])/, '|')
@s = @raw.gsub(/\s*\|\d+-\d+\||\|-?\d+\|\s*/, ' ').gsub(/\s+/, ' ')
@scores[:rr] = -1.0/0
end
end
def structured_update model, hypothesis, oracle, learning_rate
if hypothesis.s != oracle.s
model += (oracle.f - hypothesis.f) * learning_rate
return [model, 1]
end
return [model, 0]
end
def ranking_update w, hypothesis, oracle, learning_rate
if oracle.scores[:rr] <= hypothesis.scores[:rr] \
&& oracle.s != hypothesis.s
model += (oracle.f - hypothesis.f) * learning_rate
return [model, 1]
end
return [model, 0]
end
def read_additional_phrase_pairs fn
f = ReadFile.new fn
add = {}
while line = f.gets
id, phrase_pair = line.split ' ', 2
id = id.to_i-1
s, t = splitpipe phrase_pair, 3
phrase_pair = "#{s.strip} ||| #{t.strip}"
if add.has_key? id
add[id] << phrase_pair
else
add[id] = [phrase_pair]
end
end
return add
end
def usage
STDERR.write "#{__FILE__} <config file>\n"
exit 1
end
def main
usage if ARGV.size != 1
cfg = read_config ARGV[0]
sources = ReadFile.readlines cfg['sources']
oracles = ReadFile.readlines cfg['oracles']
kbest_lists = read_kbest_lists cfg['kbest_lists'], MosesKbestEntryWithPhraseAlignment
learning_rate = cfg['learning_rate'].to_f
learning_rate = 1.0 if !learning_rate
iterations = cfg['iterate'].to_i
output = WriteFile.new cfg['output']
output_model = cfg['output_model']
silent = true if cfg['silent']
verbose = true if cfg['verbose']
cheat = true if cfg['cheat']
additional_phrase_pairs = nil
if cfg['additional_phrase_pairs']
additional_phrase_pairs = read_additional_phrase_pairs cfg['additional_phrase_pairs']
end
ff = FeatureFactory.new cfg
if !silent
STDERR.write "Running online-reranker with config '#{File.expand_path ARGV[0]}'\n"
cfg.each_pair { |k,v| STDERR.write " #{k} = #{v}\n" }
STDERR.write "\n"
end
model = SparseVector.new
if cfg['init_model']
model.from_s ReadFile.read cfg['init_model']
end
sz = sources.size
start = Time.now
iterations.times {
|t|
overall_errors = 0
STDERR.write "Iteration #{t+1} of #{iterations}\n"
sources.each_with_index { |i,j|
STDERR.write " #{j+1}\n" if (j+1)%10==0 && !silent&&!verbose
ff.add_phrase_pairs(additional_phrase_pairs[j]) if additional_phrase_pairs
kbest = kbest_lists[j]
kbest.each { |k|
k.f = ff.produce k, sources[j]
k.score model
}
hypothesis = kbest[ kbest.map{ |k| k.scores[:rr] }.max_index ]
if !cheat
output.write "#{hypothesis.s}\n"
end
oracle = ConstrainedSearchOracle.from_s oracles[j]
oracle.f = ff.produce oracle, sources[j]
oracle.score model
err = 0
case cfg['update']
when 'structured'
model, err = structured_update model, hypothesis, oracle, learning_rate
when 'ranking'
model, err = ranking_update model, hypothesis, oracle, learning_rate
else
STDERR.write "Don't know update method '#{cfg['update']}', exiting.\n"
exit 1
end
overall_errors += err
if cheat
kbest.each { |k| k.score model }
hypothesis = kbest[ kbest.map{ |k| k.scores[:rr] }.max_index ]
output.write "#{hypothesis.s}\n"
end
if verbose
counts = { 'PP'=>0, 'NG'=>0 }
model.each_pair { |k,v|
counts[k.split(':').first] += 1
}
STDERR.write "errors=#{overall_errors}; model size=#{model.size} (PP #{counts['PP']}, ng #{counts['NG']})\n" if verbose
end
}
}
elapsed = Time.now - start
STDERR.write"#{elapsed.round 2} s, #{(elapsed/Float(sz)).round 2} s per kbest; model size: #{model.size}\n\n" if !silent
WriteFile.write model.to_s+"\n", output_model if output_model
output.close
end
main
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