PCQRSCANER/venv/Lib/site-packages/nltk/test/unit/translate/test_nist.py
2019-12-22 21:51:47 +01:00

38 lines
1.6 KiB
Python

# -*- coding: utf-8 -*-
"""
Tests for NIST translation evaluation metric
"""
import io
import unittest
from nltk.data import find
from nltk.translate.nist_score import sentence_nist, corpus_nist
class TestNIST(unittest.TestCase):
def test_sentence_nist(self):
ref_file = find('models/wmt15_eval/ref.ru')
hyp_file = find('models/wmt15_eval/google.ru')
mteval_output_file = find('models/wmt15_eval/mteval-13a.output')
# Reads the NIST scores from the `mteval-13a.output` file.
# The order of the list corresponds to the order of the ngrams.
with open(mteval_output_file, 'r') as mteval_fin:
# The numbers are located in the last 4th line of the file.
# The first and 2nd item in the list are the score and system names.
mteval_nist_scores = map(float, mteval_fin.readlines()[-4].split()[1:-1])
with io.open(ref_file, 'r', encoding='utf8') as ref_fin:
with io.open(hyp_file, 'r', encoding='utf8') as hyp_fin:
# Whitespace tokenize the file.
# Note: split() automatically strip().
hypotheses = list(map(lambda x: x.split(), hyp_fin))
# Note that the corpus_bleu input is list of list of references.
references = list(map(lambda x: [x.split()], ref_fin))
# Without smoothing.
for i, mteval_nist in zip(range(1, 10), mteval_nist_scores):
nltk_nist = corpus_nist(references, hypotheses, i)
# Check that the NIST scores difference is less than 0.5
assert abs(mteval_nist - nltk_nist) < 0.05