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#!/usr/bin/env python
# compute Bleu scores with confidence intervals via boostrap resampling
# written by Ulrich Germann
from argparse import ArgumentParser
import math
import os
from random import randint
import sys
def count_ngrams(snt, max_n):
"""
Return a dictionary of ngram counts (up to length /max_n/)
for sentence (list of words) /snt/.
"""
ret = {}
for i in xrange(len(snt)):
for k in xrange(i + 1, min(i + max_n + 1, len(snt) + 1)):
key = tuple(snt[i:k])
ret[key] = ret.get(key, 0) + 1
return ret
def max_counts(ng1, ng2):
"""
Return a dicitonary of ngram counts such that
each count is the greater of the two individual counts
for each ngram in the input ngram count dictionaries
/ng1/ and /ng2/.
"""
ret = ng1.copy()
for k, v in ng2.items():
ret[k] = max(ret.get(k, 0), v)
return ret
def ng_hits(hyp, ref, max_n):
"""
Return a list of ngram counts such that each ngram count
is the minimum of the counts in hyp and ref, up to ngram
length /max_n/.
"""
ret = [0 for i in xrange(max_n)]
for ng, cnt in hyp.items():
k = ng
if len(k) <= max_n:
ret[len(k) - 1] += min(cnt, ref.get(ng, 0))
return ret
class BleuScore:
def __init__(self, hyp, ref, max_n=4, bootstrap=1000):
# print len(hyp.ngrams), len(ref.ngrams), "X"
self.hits = [
ng_hits(hyp.ngrams[i], ref.ngrams[i], max_n)
for i in xrange(len(hyp.ngrams))]
self.max_n = max_n
self.hyp = hyp
self.ref = ref
self.lower = None
self.upper = None
self.median = None
self.bootstrap = [
self.score([randint(0, len(hyp.snt) - 1) for s in hyp.snt])
for i in xrange(1000)]
self.bootstrap.sort()
self.actual = self.score([i for i in xrange(len(hyp.snt))])
def score(self, sample):
hits = [0 for i in xrange(self.max_n)]
self.hyplen = 0
self.reflen = 0
for i in sample:
self.hyplen += len(self.hyp.snt[i])
self.reflen += len(self.ref.snt[i])
for n in xrange(self.max_n):
hits[n] += self.hits[i][n]
self.prec = [float(hits[n]) / (self.hyplen - n * len(sample))
for n in xrange(self.max_n)]
ret = sum([math.log(x) for x in self.prec]) / self.max_n
self.BP = min(
1, math.exp(1. - float(self.reflen) / float(self.hyplen)))
ret += math.log(self.BP)
return math.exp(ret)
class Document:
def __init__(self, fname=None):
self.fname = fname
if fname:
self.snt = [line.strip().split() for line in open(fname)]
self.ngrams = [count_ngrams(snt, 4) for snt in self.snt]
else:
self.snt = None
self.ngrams = None
def merge(self, R):
self.fname = "multi-ref"
self.ngrams = [x for x in R[0].ngrams]
self.snt = [x for x in R[0].snt]
for i in xrange(len(R[0].ngrams)):
for k in xrange(1, len(R)):
self.ngrams[i] = max_counts(self.ngrams[i], R[k].ngrams[i])
def update(self, hyp, R):
for i, hyp_snt in enumerate(hyp.snt):
clen = len(hyp_snt)
K = 0
for k in xrange(1, len(R)):
k_snt = R[k].snt[i]
assert len(R[k].snt) == len(hyp.snt), (
"Mismatch in number of sentences " +
"between reference and candidate")
if abs(len(k_snt) - clen) == abs(len(R[K].snt[i]) - clen):
if len(k_snt) < len(R[K].snt[i]):
K = k
elif abs(len(k_snt) - clen) < abs(len(R[K].snt[i]) - clen):
K = k
self.snt[i] = R[K].snt[i]
if __name__ == "__main__":
argparser = ArgumentParser()
argparser.add_argument(
"-r", "--ref", nargs='+', help="Reference translation(s).")
argparser.add_argument(
"-c", "--cand", nargs='+', help="Candidate translations.")
argparser.add_argument(
"-i", "--individual", action='store_true',
help="Compute BLEU scores for individual references.")
argparser.add_argument(
"-b", "--bootstrap", type=int, default=1000,
help="Sample size for bootstrap resampling.")
argparser.add_argument(
"-a", "--alpha", type=float, default=.05,
help="1-alpha = confidence interval.")
args = argparser.parse_args(sys.argv[1:])
R = [Document(fname) for fname in args.ref]
C = [Document(fname) for fname in args.cand]
Rx = Document() # for multi-reference BLEU
Rx.merge(R)
for c in C:
# compute multi-reference BLEU
Rx.update(c, R)
bleu = BleuScore(c, Rx, bootstrap=args.bootstrap)
print "%5.2f %s [%5.2f-%5.2f; %5.2f] %s" % (
100 * bleu.actual,
os.path.basename(Rx.fname),
100 * bleu.bootstrap[int((args.alpha / 2) * args.bootstrap)],
100 * bleu.bootstrap[int((1 - (args.alpha / 2)) * args.bootstrap)],
100 * bleu.bootstrap[int(.5 * args.bootstrap)],
c.fname) # os.path.basename(c.fname))
if args.individual:
for r in R:
bleu = BleuScore(c, r, bootstrap=args.bootstrap)
print " %5.2f %s" % (
100 * bleu.actual, os.path.basename(r.fname))
# print bleu.prec, bleu.hyplen, bleu.reflen, bleu.BP
# print [
# sum([bleu.hits[i][n] for i in xrange(len(bleu.hits))])
# for n in xrange(4)]
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