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bleu.py
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bleu.py
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import tempfile
from os.path import join
import subprocess
import re
import sys
import os
import numpy as np
BLEU_PATH = join("scripts", "multi-bleu.perl")
BASE_REF_FNAME = "ref"
HYP_FNAME = "hyp"
def call_bleu(base_ref_fname, hyp_fname):
command = "perl %s %s < %s" % (BLEU_PATH, base_ref_fname, hyp_fname)
result = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\
.stdout.read().decode("utf-8")
match = re.match("BLEU = ([\d.]+),.*BP=([\d.]+),.*\)", result)
if match is not None:
# print(match.group(0))
bleu = float(match.group(1))
brevity_penalty = float(match.group(2))
unpenalized = 0
if brevity_penalty != 0:
unpenalized = bleu / brevity_penalty
return bleu, unpenalized
else:
sys.stderr.write(
"warning: BLEU score not found in output file, returning 0")
return 0, 0
def read_file(fname):
with open(fname) as f:
return [line.split() for line in f]
def multi_bleu(multiple_references, hypotheses):
dir = tempfile.mkdtemp()
num_refs = len(multiple_references[0])
assert(all(len(l) == num_refs for l in multiple_references))
base_ref_fname = join(dir, BASE_REF_FNAME)
for i in range(num_refs):
ref_fname = base_ref_fname + str(i)
with open(ref_fname, 'w') as f:
for refs in multiple_references:
f.write("%s\n" % ' '.join(refs[i]))
hyp_fname = join(dir, HYP_FNAME)
with open(hyp_fname, 'w') as f:
for hyp in hypotheses:
f.write("%s\n" % ' '.join(hyp))
bleu, unpenalized_bleu = call_bleu(base_ref_fname, hyp_fname)
# clean up
for i in range(num_refs):
ref_fname = base_ref_fname + str(i)
os.remove(ref_fname)
os.remove(hyp_fname)
os.rmdir(dir)
return bleu, unpenalized_bleu
def single_bleu( references, hypotheses):
return multi_bleu([[ref] for ref in references], hypotheses)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("ref_fname")
parser.add_argument("hyp_fname")
parser.add_argument("--sentence_level", action='store_true')
parser.add_argument("--nltk", action='store_true')
args = parser.parse_args()
refs = read_file(args.ref_fname)
hyps = read_file(args.hyp_fname)
# f_bleu = call_bleu(args.ref_fname, args.hyp_fname)
# l_bleu = multi_bleu([[r] for r in refs], hyps)
# c_bleu = single_bleu(refs, hyps)
# assert(f_bleu == l_bleu == c_bleu)
if args.sentence_level:
scores = []
for (ref, hyp) in zip(refs, hyps):
if args.nltk:
import nltk
scores.append(
nltk.translate.bleu_score.sentence_bleu([ref], hyp))
else:
scores.append(single_bleu([ref], [hyp])[0])
result = np.mean(scores)
else:
if args.nltk:
import nltk
result = nltk.translate.bleu_score.corpus_bleu(
[[r] for r in refs], hyps)
result = single_bleu(refs, hyps)[0]
print(result)