bleu.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',encoding='utf-8')

	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()))

	sys.stderr.write('Total: ' + str(len(goldMap)) + '\n')
	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__':
	reference_file = sys.argv[1]
	predictions = []
	for row in sys.stdin:
		predictions.append(row)
	(goldMap, predictionMap) = computeMaps(predictions, reference_file)
	print(bleuFromMaps(goldMap, predictionMap)[0])