from nltk.translate.ribes_score import corpus_ribes, word_rank_alignment
[docs]def test_ribes_empty_worder(): # worder as in word order
# Verifies that these two sentences have no alignment,
# and hence have the lowest possible RIBES score.
hyp = "This is a nice sentence which I quite like".split()
ref = "Okay well that's neat and all but the reference's different".split()
assert word_rank_alignment(ref, hyp) == []
list_of_refs = [[ref]]
hypotheses = [hyp]
assert corpus_ribes(list_of_refs, hypotheses) == 0.0
[docs]def test_ribes_one_worder():
# Verifies that these two sentences have just one match,
# and the RIBES score for this sentence with very little
# correspondence is 0.
hyp = "This is a nice sentence which I quite like".split()
ref = "Okay well that's nice and all but the reference's different".split()
assert word_rank_alignment(ref, hyp) == [3]
list_of_refs = [[ref]]
hypotheses = [hyp]
assert corpus_ribes(list_of_refs, hypotheses) == 0.0
[docs]def test_ribes_two_worder():
# Verifies that these two sentences have two matches,
# but still get the lowest possible RIBES score due
# to the lack of similarity.
hyp = "This is a nice sentence which I quite like".split()
ref = "Okay well that's nice and all but the reference is different".split()
assert word_rank_alignment(ref, hyp) == [9, 3]
list_of_refs = [[ref]]
hypotheses = [hyp]
assert corpus_ribes(list_of_refs, hypotheses) == 0.0
[docs]def test_ribes():
# Based on the doctest of the corpus_ribes function
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",
]
list_of_refs = [[ref1a, ref1b, ref1c], [ref2a]]
hypotheses = [hyp1, hyp2]
score = corpus_ribes(list_of_refs, hypotheses)
assert round(score, 4) == 0.3597
[docs]def test_no_zero_div():
# Regression test for Issue 2529, assure that no ZeroDivisionError is thrown.
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"]
ref2a = ["he", "was", "interested", "in", "world", "history", "because", "he"]
list_of_refs = [[ref1a, ref1b, ref1c], [ref2a]]
hypotheses = [hyp1, hyp2]
score = corpus_ribes(list_of_refs, hypotheses)
assert round(score, 4) == 0.1688