B-Norm.py

#!/usr/bin/python

'''
This script was adapted from the original version by hieuhoang1972 which is part of MOSES. 
'''

# $Id: bleu.py 1307 2007-03-14 22:22:36Z hieuhoang1972 $

'''Provides:

cook_refs(refs, n=4): Transform a list of reference sentences as strings into a form usable by cook_test().
cook_test(test, refs, n=4): Transform a test sentence as a string (together with the cooked reference sentences) into a form usable by score_cooked().
score_cooked(alltest, n=4): Score a list of cooked test sentences.

score_set(s, testid, refids, n=4): Interface with dataset.py; calculate BLEU score of testid against refids.

The reason for breaking the BLEU computation into three phases cook_refs(), cook_test(), and score_cooked() is to allow the caller to calculate BLEU scores for multiple test sets as efficiently as possible.
'''

import sys, math, re, xml.sax.saxutils
import subprocess
import os

# Added to bypass NIST-style pre-processing of hyp and ref files -- wade
nonorm = 0

preserve_case = False
eff_ref_len = "shortest"

normalize1 = [
    ('<skipped>', ''),         # strip "skipped" tags
    (r'-\n', ''),              # strip end-of-line hyphenation and join lines
    (r'\n', ' '),              # join lines
#    (r'(\d)\s+(?=\d)', r'\1'), # join digits
]
normalize1 = [(re.compile(pattern), replace) for (pattern, replace) in normalize1]

normalize2 = [
    (r'([\{-\~\[-\` -\&\(-\+\:-\@\/])',r' \1 '), # tokenize punctuation. apostrophe is missing
    (r'([^0-9])([\.,])',r'\1 \2 '),              # tokenize period and comma unless preceded by a digit
    (r'([\.,])([^0-9])',r' \1 \2'),              # tokenize period and comma unless followed by a digit
    (r'([0-9])(-)',r'\1 \2 ')                    # tokenize dash when preceded by a digit
]
normalize2 = [(re.compile(pattern), replace) for (pattern, replace) in normalize2]

def normalize(s):
    '''Normalize and tokenize text. This is lifted from NIST mteval-v11a.pl.'''
    # Added to bypass NIST-style pre-processing of hyp and ref files -- wade
    if (nonorm):
        return s.split()
    if type(s) is not str:
        s = " ".join(s)
    # language-independent part:
    for (pattern, replace) in normalize1:
        s = re.sub(pattern, replace, s)
    s = xml.sax.saxutils.unescape(s, {'&quot;':'"'})
    # language-dependent part (assuming Western languages):
    s = " %s " % s
    if not preserve_case:
        s = s.lower()         # this might not be identical to the original
    for (pattern, replace) in normalize2:
        s = re.sub(pattern, replace, s)
    return s.split()

def count_ngrams(words, n=4):
    counts = {}
    for k in range(1,n+1):
        for i in range(len(words)-k+1):
            ngram = tuple(words[i:i+k])
            counts[ngram] = counts.get(ngram, 0)+1
    return counts

def cook_refs(refs, n=4):
    '''Takes a list of reference sentences for a single segment
    and returns an object that encapsulates everything that BLEU
    needs to know about them.'''
    
    refs = [normalize(ref) for ref in refs]
    maxcounts = {}
    for ref in refs:
        counts = count_ngrams(ref, n)
        for (ngram,count) in counts.items():
            maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
    return ([len(ref) for ref in refs], maxcounts)

def cook_test(test, item, n=4):
    '''Takes a test sentence and returns an object that
    encapsulates everything that BLEU needs to know about it.'''
    (reflens, refmaxcounts)=item
    test = normalize(test)
    result = {}
    result["testlen"] = len(test)

    # Calculate effective reference sentence length.
    
    if eff_ref_len == "shortest":
        result["reflen"] = min(reflens)
    elif eff_ref_len == "average":
        result["reflen"] = float(sum(reflens))/len(reflens)
    elif eff_ref_len == "closest":
        min_diff = None
        for reflen in reflens:
            if min_diff is None or abs(reflen-len(test)) < min_diff:
                min_diff = abs(reflen-len(test))
                result['reflen'] = reflen

    result["guess"] = [max(len(test)-k+1,0) for k in range(1,n+1)]

    result['correct'] = [0]*n
    counts = count_ngrams(test, n)
    for (ngram, count) in counts.items():
        result["correct"][len(ngram)-1] += min(refmaxcounts.get(ngram,0), count)

    return result

def score_cooked(allcomps, n=4, ground=0, smooth=1):
    totalcomps = {'testlen':0, 'reflen':0, 'guess':[0]*n, 'correct':[0]*n}
    for comps in allcomps:
        for key in ['testlen','reflen']:
            totalcomps[key] += comps[key]
        for key in ['guess','correct']:
            for k in range(n):
                totalcomps[key][k] += comps[key][k]
    logbleu = 0.0
    all_bleus = []
    for k in range(n):
      correct = totalcomps['correct'][k]
      guess = totalcomps['guess'][k]
      addsmooth = 0
      if smooth == 1 and k > 0:
        addsmooth = 1
      logbleu += math.log(correct + addsmooth + sys.float_info.min)-math.log(guess + addsmooth+ sys.float_info.min)
      if guess == 0:
        all_bleus.append(-10000000)
      else:
        all_bleus.append(math.log(correct + sys.float_info.min)-math.log( guess ))

    logbleu /= float(n)
    all_bleus.insert(0, logbleu)

    brevPenalty = min(0,1-float(totalcomps['reflen'] + 1)/(totalcomps['testlen'] + 1))
    for i in range(len(all_bleus)):
      if i ==0:
        all_bleus[i] += brevPenalty
      all_bleus[i] = math.exp(all_bleus[i])
    return all_bleus

def bleu(refs,  candidate, ground=0, smooth=1):
    refs = cook_refs(refs)
    test = cook_test(candidate, refs)
    return score_cooked([test], ground=ground, smooth=smooth)

def splitPuncts(line):
  return ' '.join(re.findall(r"[\w]+|[^\s\w]", line))

def computeMaps(predictions, goldfile):
  predictionMap = {}
  goldMap = {}
  gf = open(goldfile, 'r')

  for row in predictions:
    cols = row.strip().split('\t')
    if len(cols) == 1:
      (rid, pred) = (cols[0], '') 
    else:
      (rid, pred) = (cols[0], cols[1]) 
    predictionMap[rid] = [splitPuncts(pred.strip().lower())]

  for row in gf:
    (rid, pred) = row.split('\t') 
    if rid in predictionMap: # Only insert if the id exists for the method
      if rid not in goldMap:
        goldMap[rid] = []
      goldMap[rid].append(splitPuncts(pred.strip().lower()))

  return (goldMap, predictionMap)


#m1 is the reference map
#m2 is the prediction map
def bleuFromMaps(m1, m2):
  score = [0] * 5
  num = 0.0

  for key in m1:
    if key in m2:
      bl = bleu(m1[key], m2[key][0])
      score = [ score[i] + bl[i] for i in range(0, len(bl))]
      num += 1
  return [s * 100.0 / num for s in score]

if __name__ == '__main__':
  ref_sentence_lst = open(sys.argv[1]).read().split("\n")
  with open("tmp_ref.txt","w") as f:
    for idx, ref_sentence in enumerate(ref_sentence_lst):
      f.write("{}\t{}\n".format(idx, ref_sentence))

  reference_file = "tmp_ref.txt"
  predictions = []
  for idx, row in enumerate(sys.stdin):
    predictions.append("{}\t{}".format(idx,row))
  if len(predictions)==len(ref_sentence_lst)-1:
    predictions.append("{}\t{}".format(len(predictions),""))
  (goldMap, predictionMap) = computeMaps(predictions, reference_file) 
  print (bleuFromMaps(goldMap, predictionMap)[0])
  os.remove("tmp_ref.txt")