Source code for nltk.test.unit.translate.test_nist

Tests for NIST translation evaluation metric

import io
import unittest

from import find
from nltk.translate.nist_score import corpus_nist

[docs]class TestNIST(unittest.TestCase):
[docs] def test_sentence_nist(self): ref_file = find("models/wmt15_eval/") hyp_file = find("models/wmt15_eval/") 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) 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 open(ref_file, encoding="utf8") as ref_fin: with open(hyp_file, 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