"""
Tests for BLEU translation evaluation metric
"""
import io
import unittest
from nltk.data import find
from nltk.translate.bleu_score import (
SmoothingFunction,
brevity_penalty,
closest_ref_length,
corpus_bleu,
modified_precision,
sentence_bleu,
)
[docs]class TestBLEU(unittest.TestCase):
[docs] def test_modified_precision(self):
"""
Examples from the original BLEU paper
https://www.aclweb.org/anthology/P02-1040.pdf
"""
# Example 1: the "the*" example.
# Reference sentences.
ref1 = "the cat is on the mat".split()
ref2 = "there is a cat on the mat".split()
# Hypothesis sentence(s).
hyp1 = "the the the the the the the".split()
references = [ref1, ref2]
# Testing modified unigram precision.
hyp1_unigram_precision = float(modified_precision(references, hyp1, n=1))
assert round(hyp1_unigram_precision, 4) == 0.2857
# With assertAlmostEqual at 4 place precision.
self.assertAlmostEqual(hyp1_unigram_precision, 0.28571428, places=4)
# Testing modified bigram precision.
assert float(modified_precision(references, hyp1, n=2)) == 0.0
# Example 2: the "of the" example.
# Reference sentences
ref1 = str(
"It is a guide to action that ensures that the military "
"will forever heed Party commands"
).split()
ref2 = str(
"It is the guiding principle which guarantees the military "
"forces always being under the command of the Party"
).split()
ref3 = str(
"It is the practical guide for the army always to heed "
"the directions of the party"
).split()
# Hypothesis sentence(s).
hyp1 = "of the".split()
references = [ref1, ref2, ref3]
# Testing modified unigram precision.
assert float(modified_precision(references, hyp1, n=1)) == 1.0
# Testing modified bigram precision.
assert float(modified_precision(references, hyp1, n=2)) == 1.0
# Example 3: Proper MT outputs.
hyp1 = str(
"It is a guide to action which ensures that the military "
"always obeys the commands of the party"
).split()
hyp2 = str(
"It is to insure the troops forever hearing the activity "
"guidebook that party direct"
).split()
references = [ref1, ref2, ref3]
# Unigram precision.
hyp1_unigram_precision = float(modified_precision(references, hyp1, n=1))
hyp2_unigram_precision = float(modified_precision(references, hyp2, n=1))
# Test unigram precision with assertAlmostEqual at 4 place precision.
self.assertAlmostEqual(hyp1_unigram_precision, 0.94444444, places=4)
self.assertAlmostEqual(hyp2_unigram_precision, 0.57142857, places=4)
# Test unigram precision with rounding.
assert round(hyp1_unigram_precision, 4) == 0.9444
assert round(hyp2_unigram_precision, 4) == 0.5714
# Bigram precision
hyp1_bigram_precision = float(modified_precision(references, hyp1, n=2))
hyp2_bigram_precision = float(modified_precision(references, hyp2, n=2))
# Test bigram precision with assertAlmostEqual at 4 place precision.
self.assertAlmostEqual(hyp1_bigram_precision, 0.58823529, places=4)
self.assertAlmostEqual(hyp2_bigram_precision, 0.07692307, places=4)
# Test bigram precision with rounding.
assert round(hyp1_bigram_precision, 4) == 0.5882
assert round(hyp2_bigram_precision, 4) == 0.0769
[docs] def test_brevity_penalty(self):
# Test case from brevity_penalty_closest function in mteval-v13a.pl.
# Same test cases as in the doctest in nltk.translate.bleu_score.py
references = [["a"] * 11, ["a"] * 8]
hypothesis = ["a"] * 7
hyp_len = len(hypothesis)
closest_ref_len = closest_ref_length(references, hyp_len)
self.assertAlmostEqual(
brevity_penalty(closest_ref_len, hyp_len), 0.8669, places=4
)
references = [["a"] * 11, ["a"] * 8, ["a"] * 6, ["a"] * 7]
hypothesis = ["a"] * 7
hyp_len = len(hypothesis)
closest_ref_len = closest_ref_length(references, hyp_len)
assert brevity_penalty(closest_ref_len, hyp_len) == 1.0
[docs] def test_zero_matches(self):
# Test case where there's 0 matches
references = ["The candidate has no alignment to any of the references".split()]
hypothesis = "John loves Mary".split()
# Test BLEU to nth order of n-grams, where n is len(hypothesis).
for n in range(1, len(hypothesis)):
weights = (1.0 / n,) * n # Uniform weights.
assert sentence_bleu(references, hypothesis, weights) == 0
[docs] def test_full_matches(self):
# Test case where there's 100% matches
references = ["John loves Mary".split()]
hypothesis = "John loves Mary".split()
# Test BLEU to nth order of n-grams, where n is len(hypothesis).
for n in range(1, len(hypothesis)):
weights = (1.0 / n,) * n # Uniform weights.
assert sentence_bleu(references, hypothesis, weights) == 1.0
[docs] def test_partial_matches_hypothesis_longer_than_reference(self):
references = ["John loves Mary".split()]
hypothesis = "John loves Mary who loves Mike".split()
# Since no 4-grams matches were found the result should be zero
# exp(w_1 * 1 * w_2 * 1 * w_3 * 1 * w_4 * -inf) = 0
self.assertAlmostEqual(sentence_bleu(references, hypothesis), 0.0, places=4)
# Checks that the warning has been raised because len(reference) < 4.
try:
self.assertWarns(UserWarning, sentence_bleu, references, hypothesis)
except AttributeError:
pass # unittest.TestCase.assertWarns is only supported in Python >= 3.2.
# @unittest.skip("Skipping fringe cases for BLEU.")
[docs]class TestBLEUFringeCases(unittest.TestCase):
[docs] def test_case_where_n_is_bigger_than_hypothesis_length(self):
# Test BLEU to nth order of n-grams, where n > len(hypothesis).
references = ["John loves Mary ?".split()]
hypothesis = "John loves Mary".split()
n = len(hypothesis) + 1 #
weights = (1.0 / n,) * n # Uniform weights.
# Since no n-grams matches were found the result should be zero
# exp(w_1 * 1 * w_2 * 1 * w_3 * 1 * w_4 * -inf) = 0
self.assertAlmostEqual(
sentence_bleu(references, hypothesis, weights), 0.0, places=4
)
# Checks that the warning has been raised because len(hypothesis) < 4.
try:
self.assertWarns(UserWarning, sentence_bleu, references, hypothesis)
except AttributeError:
pass # unittest.TestCase.assertWarns is only supported in Python >= 3.2.
# Test case where n > len(hypothesis) but so is n > len(reference), and
# it's a special case where reference == hypothesis.
references = ["John loves Mary".split()]
hypothesis = "John loves Mary".split()
# Since no 4-grams matches were found the result should be zero
# exp(w_1 * 1 * w_2 * 1 * w_3 * 1 * w_4 * -inf) = 0
self.assertAlmostEqual(
sentence_bleu(references, hypothesis, weights), 0.0, places=4
)
[docs] def test_empty_hypothesis(self):
# Test case where there's hypothesis is empty.
references = ["The candidate has no alignment to any of the references".split()]
hypothesis = []
assert sentence_bleu(references, hypothesis) == 0
[docs] def test_length_one_hypothesis(self):
# Test case where there's hypothesis is of length 1 in Smoothing method 4.
references = ["The candidate has no alignment to any of the references".split()]
hypothesis = ["Foo"]
method4 = SmoothingFunction().method4
try:
sentence_bleu(references, hypothesis, smoothing_function=method4)
except ValueError:
pass # unittest.TestCase.assertWarns is only supported in Python >= 3.2.
[docs] def test_empty_references(self):
# Test case where there's reference is empty.
references = [[]]
hypothesis = "John loves Mary".split()
assert sentence_bleu(references, hypothesis) == 0
[docs] def test_empty_references_and_hypothesis(self):
# Test case where both references and hypothesis is empty.
references = [[]]
hypothesis = []
assert sentence_bleu(references, hypothesis) == 0
[docs] def test_reference_or_hypothesis_shorter_than_fourgrams(self):
# Test case where the length of reference or hypothesis
# is shorter than 4.
references = ["let it go".split()]
hypothesis = "let go it".split()
# Checks that the value the hypothesis and reference returns is 0.0
# exp(w_1 * 1 * w_2 * 1 * w_3 * 1 * w_4 * -inf) = 0
self.assertAlmostEqual(sentence_bleu(references, hypothesis), 0.0, places=4)
# Checks that the warning has been raised.
try:
self.assertWarns(UserWarning, sentence_bleu, references, hypothesis)
except AttributeError:
pass # unittest.TestCase.assertWarns is only supported in Python >= 3.2.
[docs]class TestBLEUvsMteval13a(unittest.TestCase):
[docs] def test_corpus_bleu(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 BLEU 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 2nd line of the file.
# The first and 2nd item in the list are the score and system names.
mteval_bleu_scores = map(float, mteval_fin.readlines()[-2].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().
hypothesis = 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_bleu in zip(range(1, 10), mteval_bleu_scores):
nltk_bleu = corpus_bleu(
references, hypothesis, weights=(1.0 / i,) * i
)
# Check that the BLEU scores difference is less than 0.005 .
# Note: This is an approximate comparison; as much as
# +/- 0.01 BLEU might be "statistically significant",
# the actual translation quality might not be.
assert abs(mteval_bleu - nltk_bleu) < 0.005
# With the same smoothing method used in mteval-v13a.pl
chencherry = SmoothingFunction()
for i, mteval_bleu in zip(range(1, 10), mteval_bleu_scores):
nltk_bleu = corpus_bleu(
references,
hypothesis,
weights=(1.0 / i,) * i,
smoothing_function=chencherry.method3,
)
assert abs(mteval_bleu - nltk_bleu) < 0.005
[docs]class TestBLEUWithBadSentence(unittest.TestCase):
[docs] def test_corpus_bleu_with_bad_sentence(self):
hyp = "Teo S yb , oe uNb , R , T t , , t Tue Ar saln S , , 5istsi l , 5oe R ulO sae oR R"
ref = str(
"Their tasks include changing a pump on the faulty stokehold ."
"Likewise , two species that are very similar in morphology "
"were distinguished using genetics ."
)
references = [[ref.split()]]
hypotheses = [hyp.split()]
try: # Check that the warning is raised since no. of 2-grams < 0.
with self.assertWarns(UserWarning):
# Verify that the BLEU output is undesired since no. of 2-grams < 0.
self.assertAlmostEqual(
corpus_bleu(references, hypotheses), 0.0, places=4
)
except AttributeError: # unittest.TestCase.assertWarns is only supported in Python >= 3.2.
self.assertAlmostEqual(corpus_bleu(references, hypotheses), 0.0, places=4)
[docs]class TestBLEUWithMultipleWeights(unittest.TestCase):
[docs] def test_corpus_bleu_with_multiple_weights(self):
hyp1 = [
"It",
"is",
"a",
"guide",
"to",
"action",
"which",
"ensures",
"that",
"the",
"military",
"always",
"obeys",
"the",
"commands",
"of",
"the",
"party",
]
ref1a = [
"It",
"is",
"a",
"guide",
"to",
"action",
"that",
"ensures",
"that",
"the",
"military",
"will",
"forever",
"heed",
"Party",
"commands",
]
ref1b = [
"It",
"is",
"the",
"guiding",
"principle",
"which",
"guarantees",
"the",
"military",
"forces",
"always",
"being",
"under",
"the",
"command",
"of",
"the",
"Party",
]
ref1c = [
"It",
"is",
"the",
"practical",
"guide",
"for",
"the",
"army",
"always",
"to",
"heed",
"the",
"directions",
"of",
"the",
"party",
]
hyp2 = [
"he",
"read",
"the",
"book",
"because",
"he",
"was",
"interested",
"in",
"world",
"history",
]
ref2a = [
"he",
"was",
"interested",
"in",
"world",
"history",
"because",
"he",
"read",
"the",
"book",
]
weight_1 = (1, 0, 0, 0)
weight_2 = (0.25, 0.25, 0.25, 0.25)
weight_3 = (0, 0, 0, 0, 1)
bleu_scores = corpus_bleu(
list_of_references=[[ref1a, ref1b, ref1c], [ref2a]],
hypotheses=[hyp1, hyp2],
weights=[weight_1, weight_2, weight_3],
)
assert bleu_scores[0] == corpus_bleu(
[[ref1a, ref1b, ref1c], [ref2a]], [hyp1, hyp2], weight_1
)
assert bleu_scores[1] == corpus_bleu(
[[ref1a, ref1b, ref1c], [ref2a]], [hyp1, hyp2], weight_2
)
assert bleu_scores[2] == corpus_bleu(
[[ref1a, ref1b, ref1c], [ref2a]], [hyp1, hyp2], weight_3
)