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|
#!/usr/bin/env ruby
require 'trollop'
require 'tempfile'
require 'open3'
require 'memcached'
require 'timeout'
SMT_SEMPARSE = 'python /workspace/grounded/smt-semparse-cp/decode_sentence.py /workspace/grounded/smt-semparse-cp/working/full_dataset 2>/dev/null'
EVAL_PL = '/workspace/grounded/wasp-1.0/data/geo-funql/eval/eval.pl'
CDEC = "/toolbox/cdec-dtrain/bin/cdec"
$cache = Memcached.new("localhost:11211")
# the semantic parser hangs sometimes
def spawn_with_timeout cmd, t=4, debug=false
puts cmd if debug
pipe_in, pipe_out = IO.pipe
pid = Process.spawn(cmd, :out => pipe_out)
begin
Timeout.timeout(t) { Process.wait pid }
rescue Timeout::Error
return ""
# accept the zombies
#Process.kill('TERM', pid)
end
pipe_out.close
return pipe_in.read
end
# execute
def exec natural_language_string, reference_output, no_output=false
func = nil
output = nil
feedback = nil
key_prefix = natural_language_string.encode("ASCII", :invalid => :replace, :undef => :replace, :replace => "?").gsub(/ /,'_')
begin
func = $cache.get key_prefix+"__FUNC"
output = $cache.get key_prefix+"__OUTPUT"
feedback = $cache.get key_prefix+"__FEEDBACK"
rescue Memcached::NotFound
#func = spawn_with_timeout("#{SMT_SEMPARSE} \"#{natural_language_string}\"").strip
func = `#{SMT_SEMPARSE} "#{natural_language_string}"`.strip
#output = spawn_with_timeout("echo \"execute_funql_query(#{func}, X).\" | swipl -s #{EVAL_PL} 2>&1 | grep \"X =\"").strip.split('X = ')[1]
output = `echo "execute_funql_query(#{func}, X)." | swipl -s #{EVAL_PL} 2>&1 | grep "X ="`.strip.split('X = ')[1]
feedback = output==reference_output
begin
$cache.set key_prefix+"__FUNC", func
$cache.set key_prefix+"__OUTPUT", output
$cache.set key_prefix+"__FEEDBACK", feedback
rescue SystemExit, Interrupt
$cache.delete key_prefix+"__FUNC"
$cache.delete key_prefix+"__OUTPUT"
$cache.delete key_prefix+"__FEEDBACK"
end
end
puts " nrl: #{natural_language_string}" if !no_output
puts " mrl: #{func}" if !no_output
puts " output: #{output}" if !no_output
puts " correct?: #{feedback}" if !no_output
return feedback, func, output
end
# decoder interaction/translations
class Translation
attr_accessor :s, :f, :rank, :model, :score
def initialize kbest_line, rank=-1
a = kbest_line.split ' ||| '
@s = a[1].strip
h = {}
a[2].split.each { |i|
name, value = i.split '='
value = value.to_f
h[name] = value
}
@f = NamedSparseVector.new h
@rank = rank
@model = a[3].to_f
@score = -1.0
end
def to_s
"#{@rank} ||| #{@s} ||| #{@model} ||| #{@score} ||| #{@f.to_s}"
end
end
def predict_translation s, k, ini, w
o, s = Open3.capture2 "echo \"#{s}\" | #{CDEC} -c #{ini} -r -k #{k} -w #{w} 2>/dev/null"
j = -1
return o.split("\n").map{|i| j+=1; Translation.new(i, j)}
end
# scoring (per-sentence BLEU)
def ngrams_it(s, n, fix=false)
a = s.strip.split
a.each_with_index { |tok, i|
tok.strip!
0.upto([n-1, a.size-i-1].min) { |m|
yield a[i..i+m] if !(fix||(a[i..i+m].size>n))
}
}
end
def brevity_penalty hypothesis, reference
a = hypothesis.split; b = reference.split
return 1.0 if a.size>b.size
return Math.exp(1.0 - b.size.to_f/a.size);
end
def per_sentence_bleu hypothesis, reference, n=4
h_ng = {}; r_ng = {}
(1).upto(n) {|i| h_ng[i] = []; r_ng[i] = []}
ngrams_it(hypothesis, n) {|i| h_ng[i.size] << i}
ngrams_it(reference, n) {|i| r_ng[i.size] << i}
m = [n, reference.split.size].min
weight = 1.0/m
add = 0.0
sum = 0
(1).upto(m) { |i|
counts_clipped = 0
counts_sum = h_ng[i].size
h_ng[i].uniq.each {|j| counts_clipped += r_ng[i].count(j)}
add = 1.0 if i >= 2
sum += weight * Math.log((counts_clipped + add)/(counts_sum + add));
}
return brevity_penalty(hypothesis, reference) * Math.exp(sum)
end
def score_translations list_of_translations, reference
list_of_translations.each { |i| i.score = per_sentence_bleu i.s, reference}
end
# hope and fear
def hope_and_fear kbest, action
max = -1.0/0
max_idx = -1
kbest.each_with_index { |i,j|
if action=='hope' && i.model + i.score > max
max_idx = j; max = i.model + i.score
end
if action=='fear' && i.model - i.score > max
max_idx = j; max = i.model - i.score
end
}
return kbest[max_idx]
end
# update
def update w, hope, fear, eta
diff = hope.f - fear.f
diff *= eta
w += diff
return w
end
# weights
class NamedSparseVector
attr_accessor :h
def initialize init=nil
@h = {}
@h = init if init
@h.default = 0.0
end
def + other
new_h = Hash.new
new_h.update @h
ret = NamedSparseVector.new new_h
other.each_pair { |k,v| ret[k]+=v }
return ret
end
def from_file fn
f = File.new(fn, 'r')
while line = f.gets
name, value = line.strip.split
value = value.to_f
@h[name] = value
end
end
def to_file
s = []
@h.each_pair { |k,v| s << "#{k} #{v}" }
s.join("\n")+"\n"
end
def - other
new_h = Hash.new
new_h.update @h
ret = NamedSparseVector.new new_h
other.each_pair { |k,v| ret[k]-=v }
return ret
end
def * scalar
raise ArgumentError, "Arg is not numeric #{scalar}" unless scalar.is_a? Numeric
ret = NamedSparseVector.new
@h.keys.each { |k| ret[k] = @h[k]*scalar }
return ret
end
def dot other
sum = 0.0
@h.each_pair { |k,v|
sum += v * other[k]
}
return sum
end
def [] k
@h[k]
end
def []= k, v
@h[k] = v
end
def each_pair
@h.each_pair { |k,v| yield k,v }
end
def to_s
@h.to_s
end
def length
Math.sqrt(@h.values.map{|i|i*i}.inject(:+))
end
def normalize!
l = length
@h.each_pair { |k,v|
@h[k] = v/l
}
end
def size
@h.keys.size
end
end
# map models score to [0,1]
def adj_model kbest, factor
min = kbest.map{|i|i.model}.min
max = kbest.map{|i|i.model}.max
kbest.each {|i| i.model = factor*((i.model-min)/(max-min))}
end
class Stats
def initialize name
@name = name
@with_parse = 0.0
@with_output = 0.0
@correct_output = 0.0
end
def update feedback, func, output
@with_parse +=1 if func!="None"&&func!=''
@with_output +=1 if output!="null"&&output!=''
@correct_output += 1 if feedback==true
end
def print total
without_parse = total-@with_parse
<<-eos
[#{@name}]
#{@name} with parse #{((@with_parse/total)*100).round 2} abs:#{@with_parse}
#{@name} with output #{((@with_output/total)*100).round 2} abs:#{@with_output}
#{@name} with correct output #{((@correct_output/total)*100).round 2} adj:#{((@correct_output/(total-without_parse))*100).round 2} abs:#{@correct_output}
eos
end
end
def _print rank, string, model, score
puts "rank=#{rank} string='#{string}' model=#{model} score=#{score}"
end
def bag_of_words s, stopwords=[]
s.split.uniq.sort.reject{|v| stopwords.include? v}
end
def gethopefear_standard kbest, feedback
hope = fear = nil
type1 = type2 = false
if feedback == true
hope = kbest[0]
type1 = true
else
hope = hope_and_fear(kbest, 'hope')
type2 = true
end
fear = hope_and_fear(kbest, 'fear')
return hope, fear, false, type1, type2
end
def gethopefear_fear_no_exec kbest, feedback, gold, max
hope = fear = nil
type1 = type2 = false
if feedback == true
hope = kbest[0]
type1 = true
else
hope = hope_and_fear(kbest, 'hope')
type2 = true
end
kbest.sort{|x,y|(y.model+y.score)<=>(x.model+x.score)}.each_with_index { |k,i|
break if i==max
if !exec(k.s, gold, true)[0]
fear = k
break
end
}
skip=true if !fear
return hope, fear, skip, type1, type2
end
def gethopefear_fear_no_exec_skip kbest, feedback, gold
hope = fear = nil
type1 = type2 = false
if feedback == true
hope = kbest[0]
type1 = true
else
hope = hope_and_fear(kbest, 'hope')
type2 = true
end
fear = hope_and_fear(kbest, 'fear')
skip = exec(fear.s, gold, true)[0]
return hope, fear, skip, type1, type2
end
def gethopefear_fear_no_exec_hope_exec kbest, feedback, gold, max
hope = fear = nil; hope_idx = 0
type1 = type2 = false
sorted_kbest = kbest.sort{|x,y|(y.model+y.score)<=>(x.model+x.score)}
if feedback == true
hope = kbest[0]
type1 = true
else
sorted_kbest.each_with_index { |k,i|
next if i==0
break if i==max
if exec(k.s, gold, true)[0]
hope_idx = i
hope = k
break
end
}
type2 = true
end
sorted_kbest.each_with_index { |k,i|
break if i>(kbest.size-(hope_idx+1))||i==max
if !exec(k.s, gold, true)[0]
fear = k
break
end
}
skip = true if !hope||!fear
return hope, fear, skip, type1, type2
end
def gethopefear_fear_no_exec_hope_exec_skip kbest, feedback, gold, max
hope = fear = nil
type1 = type2 = false
if feedback == true
hope = kbest[0]
type1 = true
else
hope = hope_and_fear(kbest, 'hope')
type2 = true
end
fear = hope_and_fear(kbest, 'fear')
skip = exec(fear.s, gold, true)[0]||!exec(hope.s, gold, true)[0]
return hope, fear, skip, type1, type2
end
def gethopefear_only_exec kbest, feedback, gold, max, own_reference=nil
hope = fear = nil; hope_idx = 0; new_reference = nil
type1 = type2 = false
if feedback == true
hope = kbest[0]
new_reference = hope
type1 = true
elsif own_reference
hope = own_reference
type1 = true
else
kbest.each_with_index { |k,i|
next if i==0
break if i==max
if exec(k.s, gold, true)[0]
hope_idx = i
hope = k
break
end
}
type2 = true
end
kbest.each_with_index { |k,i|
next if i==0||i==hope_idx
break if i==max
if !exec(k.s, gold, true)[0]
fear = k
break
end
}
skip = true if !hope||!fear
return hope, fear, skip, type1, type2, new_reference
end
def gethopefear_only_exec_simple kbest, feedback, gold, max, own_reference=nil
hope = fear = nil; hope_idx = 0; new_reference = nil
type1 = type2 = false
if feedback == true
hope = kbest[0]
new_reference = hope
type1 = true
elsif own_reference
hope = own_reference
type1 = true
else
kbest.each_with_index { |k,i|
next if i==0
break if i==max
if exec(k.s, gold, true)[0]
hope_idx = i
hope = k
break
end
}
type2 = true
end
kbest.each_with_index { |k,i|
next if i==0||i==hope_idx
break if i==max
if !exec(k.s, gold, true)[0]
fear = k
break
end
}
skip = true if !hope||!fear
return hope, fear, skip, type1, type2, new_reference
end
def gethopefear_rampion kbest, reference
hope = fear = nil
type1 = type2 = false
if kbest[0].s == reference
hope = kbest[0]
fear = hope_and_fear(kbest, 'fear')
type1 = true
else
hope = hope_and_fear(kbest, 'hope')
fear = kbest[0]
type2 = true
end
return hope, fear, false, type1, type2
end
def main
opts = Trollop::options do
# data
opt :k, "k", :type => :int, :default => 10000
opt :hope_fear_max, "asdf", :type => :int, :default => 32, :short => '-q'
opt :input, "'foreign' input", :type => :string, :required => true
opt :references, "(parseable) references", :type => :string, :required => true
opt :gold, "gold output", :type => :string, :require => true
opt :gold_mrl, "gold parse", :type => :string, :short => '-h', :require => true
opt :init_weights, "initial weights", :type => :string, :required => true, :short => '-w'
opt :cdec_ini, "cdec config file", :type => :string, :default => './cdec.ini'
# output
opt :debug, "debug output", :type => :bool, :default => false
opt :output_weights, "output file for final weights", :type => :string, :required => true
opt :stop_after, "stop after x examples", :type => :int, :default => -1
opt :print_kbests, "print full kbest lists", :type => :bool, :default => false, :short => '-l'
# important parameters
opt :eta, "learning rate", :type => :float, :default => 0.01
opt :iterate, "iteration X epochs", :type => :int, :default => 1, :short => '-j'
opt :variant, "standard, rampion, fear_no_exec, fear_no_exec_skip, fear_no_exec_hope_exec, fear_no_exec_hope_exec_skip, only_exec", :default => 'standard'
# misc parameters
opt :scale_model, "scale model score by this factor", :type => :float, :default => 1.0, :short => '-m'
opt :normalize, "normalize weights after each update", :type => :bool, :default => false, :short => '-n'
opt :skip_on_no_proper_gold, "skip if the reference didn't produce a proper gold output", :default => false, :short => '-x'
opt :no_update, "don't update weights", :type => :bool, :default => false, :short => '-y'
end
# output configuration
puts "cfg"
opts.each_pair {|k,v| puts "#{k}=#{v}"}
puts
# read files
input = File.readlines(opts[:input], :encoding=>'utf-8').map{|i|i.strip}
references = File.readlines(opts[:references], :encoding=>'utf-8').map{|i|i.strip}
gold = File.readlines(opts[:gold], :encoding=>'utf-8').map{|i|i.strip}
gold_mrl = File.readlines(opts[:gold_mrl], :encoding=>'utf-8').map{|i|i.strip}
stopwords = File.readlines('d/stopwords.en', :encoding=>'utf-8').map{|i|i.strip}
# only_exec: new refs
own_references = nil
own_references = references.map{|i|nil} if opts[:variant]== 'only_exec'
# init weights
w = NamedSparseVector.new
w.from_file opts[:init_weights]
last_wf = ''
# iterate
opts[:iterate].times { |iter|
# numerous counters
without_translations = 0
no_proper_gold_output = 0
count = 0
top1_stats = Stats.new 'top1'
hope_stats = Stats.new 'hope'
fear_stats = Stats.new 'fear'
refs_stats = Stats.new 'refs'
type1_updates = 0
type2_updates = 0
top1_hit = 0
top1_variant = 0
top1_real_variant = 0
hope_hit = 0
hope_variant = 0
hope_real_variant = 0
kbest_sz = 0
# for each example
input.each_with_index { |i,j|
count += 1
# write current weights to file
tmp_file = Tempfile.new('rampion')
tmp_file_path = tmp_file.path
last_wf = tmp_file.path
tmp_file.write w.to_file
tmp_file.close
# get kbest list for current input
kbest = predict_translation i, opts[:k], opts[:cdec_ini], tmp_file_path
kbest_sz += kbest.size
# output
puts "EXAMPLE #{j}"
puts "GOLD MRL: #{gold_mrl[j]}"
puts "GOLD OUTPUT #{gold[j]}"
# skip if no translation could be produced
if kbest.size == 0
without_translations += 1
puts "NO MT OUTPUT, skipping example\n\n"
next
end
# no proper gold
if gold[j] == '[]' || gold[j] == '[...]' || gold[j] == '[].'
no_proper_gold_output += 1
if opts[:skip_on_no_proper_gold]
puts "NO PROPER GOLD OUTPUT, skipping example\n\n"
next
end
end
# score kbest list
score_translations kbest, references[j]
# print kbest list
if opts[:print_kbests]
puts "<<<KBEST"
kbest.each_with_index { |k,l|
_print l, k.s, k.model, k.score
}
puts ">>>"
end
# adjust model scores to fit in [0,1]
adj_model kbest, opts[:scale_model]
# top1
puts "---top1"
puts "TOP1 TRANSLATION: #{kbest[0].s}" if iter+1==opts[:iterate]
_print 0, kbest[0].s, kbest[0].model, kbest[0].score
feedback, func, output = exec kbest[0].s, gold[j]
top1_stats.update feedback, func, output
# reference as bag of words
ref_words = bag_of_words references[j], stopwords
# hope and fear
hope = fear = new_reference = nil
type1 = type2 = skip = false
if opts[:variant] == 'standard'
hope, fear, skip, type1, type2 = gethopefear_standard kbest, feedback
elsif opts[:variant] == 'rampion'
hope, fear, skip, type1, type2 = gethopefear_rampion kbest, references[j]
elsif opts[:variant] == 'fear_no_exec_skip'
hope, fear, skip, type1, type2 = gethopefear_fear_no_exec_skip kbest, feedback, gold[j]
elsif opts[:variant] == 'fear_no_exec'
hope, fear, skip, type1, type2 = gethopefear_fear_no_exec kbest, feedback, gold[j], opts[:hope_fear_max]
elsif opts[:variant] == 'fear_no_exec_hope_exec'
hope, fear, skip, type1, type2 = gethopefear_fear_no_exec_hope_exec kbest, feedback, gold[j], opts[:hope_fear_max]
elsif opts[:variant] == 'fear_no_exec_hope_exec_skip'
hope, fear, skip, type1, type2 = gethopefear_fear_no_exec_hope_exec_skip kbest, feedback, gold[j], opts[:hope_fear_max]
elsif opts[:variant] == 'only_exec'
hope, fear, skip, type1, type2, new_reference = gethopefear_only_exec kbest, feedback, gold[j], opts[:hope_fear_max], own_references[j]
else
puts "no such hope/fear variant"
exit 1
end
# new reference (only_exec)
if new_reference
own_references[j] = new_reference
end
# type1/type2
type1_updates+=1 if type1
type2_updates+=1 if type2
# top1/hope hit
if kbest[0].s == references[j]
top1_hit += 1
else
top1_variant += 1
top1_real_variant += 1 if bag_of_words(kbest[0].s,stopwords)!=ref_words
end
if hope&&hope.s == references[j]
hope_hit += 1
elsif hope
hope_variant += 1
hope_real_variant += 1 if bag_of_words(hope.s,stopwords)!=ref_words
end
# output info for current example
puts "---hope"
if hope
_print hope.rank, hope.s, hope.model, hope.score
feedback, func, output = exec hope.s, gold[j]
hope_stats.update feedback, func, output
end
puts "---fear"
if fear
_print fear.rank, fear.s, fear.model, fear.score
feedback, func, output = exec fear.s, gold[j]
fear_stats.update feedback, func, output
end
puts "---reference"
_print 'x', references[j], 'x', 1.0
feedback, func, output = exec references[j], gold[j]
refs_stats.update feedback, func, output
# skip example?
if skip||!hope||!fear
puts "NO GOOD FEAR/HOPE, skipping example\n\n"
next
end
puts
# update
w = update w, hope, fear, opts[:eta] if !opts[:no_update]
# normalize weight vector to length 1
w.normalize! if opts[:normalize]
# stopx after x examples
break if opts[:stop_after]>0 && (j+1)==opts[:stop_after]
}
# keep weight files for each iteration
if opts[:iterate] > 1
FileUtils::cp(last_wf, "#{opts[:output_weights]}.#{iter}")
else
FileUtils::cp(last_wf, opts[:output_weights])
end
# output stats
puts "iteration ##{iter+1}/#{opts[:iterate]}"
puts "#{count} examples"
puts " type1 updates: #{type1_updates}"
puts " type2 updates: #{type2_updates}"
puts " top1 hits: #{top1_hit}"
puts " top1 variant: #{top1_variant}"
puts "top1 real variant: #{top1_real_variant}"
puts " hope hits: #{hope_hit}"
puts " hope variant: #{hope_variant}"
puts "hope real variant: #{hope_real_variant}"
puts " kbest size: #{(kbest_sz/count).round 2}"
puts "#{((without_translations.to_f/count)*100).round 2}% without translations (abs: #{without_translations})"
puts "#{((no_proper_gold_output.to_f/count)*100).round 2}% no good gold output (abs: #{no_proper_gold_output})"
puts top1_stats.print count
puts hope_stats.print count
puts fear_stats.print count
puts refs_stats.print count
}
end
main
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