def test_regex_match(doc_setup):
"""Test RegexMatch matcher."""
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=2)
# a wrong option name should raise an excetiopn
with pytest.raises(Exception):
RegexMatchSpan(regex=r"apple")
# Test if matcher raises an error when _f is given non-TemporarySpanMention
matcher = RegexMatchSpan(rgx=r"apple")
with pytest.raises(ValueError):
list(matcher.apply(doc.sentences[0].words))
matcher = RegexMatchEach(rgx=r"apple")
with pytest.raises(ValueError):
list(matcher.apply(doc.sentences[0].words))
# Test if RegexMatchEach works as expected.
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"apple"}
# Test ignore_case option
matcher = RegexMatchEach(rgx=r"Apple", ignore_case=False)
assert list(matcher.apply(space.apply(doc))) == []
# Test sep option
matcher = RegexMatchSpan(rgx=r"isapple", sep=" ")
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"is apple"}
def test_union(doc_setup):
"""Test union matcher."""
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=2)
tc: TemporarySpanMention
assert set(tc.get_span() for tc in space.apply(doc)) == {
"This is",
"is apple",
"This",
"is",
"apple",
}
# Match any span that contains "apple"
matcher0 = RegexMatchSpan(rgx=r"apple",
search=True,
full_match=True,
longest_match_only=False)
assert set(tc.get_span() for tc in matcher0.apply(space.apply(doc))) == {
"is apple",
"apple",
}
# Match any span that contains "this" (case insensitive)
matcher1 = RegexMatchSpan(rgx=r"this",
search=False,
full_match=False,
longest_match_only=False)
assert set(tc.get_span() for tc in matcher1.apply(space.apply(doc))) == {
"This is",
"This",
}
matcher = Union(matcher0, matcher1, longest_match_only=False)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"is apple",
"apple",
"This is",
"This",
}
# longest_match_only of each matcher is ignored.
matcher = Union(matcher0, matcher1, longest_match_only=True)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"This is",
"is apple",
}
# Unsupported option should raise an exception
with pytest.raises(Exception):
Union(matcher0, matcher1, long_match_only=False)
def test_ancestors(doc_setup, mention_ids, output_common_ancestor,
output_lcad):
"""Test if get_vert_ngrams works."""
doc = doc_setup
# Create 1-gram span mentions
space = MentionNgrams(n_min=1, n_max=1)
mentions = [tc for tc in space.apply(doc)]
assert len(mentions) == len(
[word for sent in doc.sentences for word in sent.words])
# Test mentions extraction
assert mentions[0].sentence.text == "test1"
assert mentions[1].sentence.text == "test2"
assert mentions[2].sentence.text == "test3"
assert mentions[3].sentence.text == "test4"
assert mentions[4].sentence.text == "test5"
assert mentions[5].sentence.text == "test6"
assert mentions[6].sentence.text == "test7"
assert mentions[7].sentence.text == "test8 test9"
assert mentions[7].get_span() == "test8"
assert mentions[8].get_span() == "test9"
test_mentions = ([mentions[i] for i in mention_ids]
if len(mention_ids) > 0 else mentions)
# Test commont ancestor calculation
overall_common_ancestor = common_ancestor(test_mentions)
assert overall_common_ancestor == output_common_ancestor
# Test lowest commont ancestor depth calculation
overall_lowest_common_ancestor_depth = lowest_common_ancestor_depth(
test_mentions)
assert overall_lowest_common_ancestor_depth == output_lcad
def test_union(caplog, doc_setup):
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=2)
tc: TemporarySpanMention
assert set(tc.get_span() for tc in space.apply(doc)) == {
"This is",
"is apple",
"This",
"is",
"apple",
}
# Match any span that contains "apple"
matcher0 = RegexMatchSpan(rgx=r"apple",
search=True,
full_match=True,
longest_match_only=False)
assert set(tc.get_span() for tc in matcher0.apply(space.apply(doc))) == {
"is apple",
"apple",
}
# Match any span that contains "this" (case insensitive)
matcher1 = RegexMatchSpan(rgx=r"this",
search=False,
full_match=False,
longest_match_only=False)
assert set(tc.get_span() for tc in matcher1.apply(space.apply(doc))) == {
"This is",
"This",
}
matcher = Union(matcher0, matcher1, longest_match_only=False)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"is apple",
"apple",
"This is",
"This",
}
# longest_match_only of each matcher is ignored.
matcher = Union(matcher0, matcher1, longest_match_only=True)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"This is",
"is apple",
}
def test_intersect(doc_setup):
"""Test intersect matcher."""
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=3)
tc: TemporarySpanMention
# Match any span that contains "apple"
matcher0 = RegexMatchSpan(rgx=r"apple",
search=True,
full_match=True,
longest_match_only=False)
assert set(tc.get_span() for tc in matcher0.apply(space.apply(doc))) == {
"This is apple",
"is apple",
"apple",
}
# Match any span that contains "this" (case insensitive)
matcher1 = RegexMatchSpan(rgx=r"this",
search=False,
full_match=False,
longest_match_only=False)
assert set(tc.get_span() for tc in matcher1.apply(space.apply(doc))) == {
"This is apple",
"This is",
"This",
}
# Intersection of matcher0 and matcher1
matcher = Intersect(matcher0, matcher1, longest_match_only=False)
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"This is apple"}
# Intersection of matcher0 and matcher0
matcher = Intersect(matcher0, matcher0, longest_match_only=False)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"This is apple",
"is apple",
"apple",
}
# longest_match_only=True overrides that of child matchers.
matcher = Intersect(matcher0, matcher0, longest_match_only=True)
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"This is apple"}
def test_inverse(doc_setup):
"""Test inverse matcher."""
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=2)
tc: TemporarySpanMention
assert set(tc.get_span() for tc in space.apply(doc)) == {
"This is",
"is apple",
"This",
"is",
"apple",
}
# Match any span that contains "apple" with longest_match_only=False
matcher0 = RegexMatchSpan(
rgx=r"apple", search=True, full_match=True, longest_match_only=False
)
assert set(tc.get_span() for tc in matcher0.apply(space.apply(doc))) == {
"is apple",
"apple",
}
# Take an inverse
matcher = Inverse(matcher0, longest_match_only=False)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"This is",
"This",
"is",
}
# longest_match_only=True
matcher = Inverse(matcher0, longest_match_only=True)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {"This is"}
# Match any span that contains "apple" with longest_match_only=True
matcher0 = RegexMatchSpan(
rgx=r"apple", search=True, full_match=True, longest_match_only=True
)
assert set(tc.get_span() for tc in matcher0.apply(space.apply(doc))) == {"is apple"}
# longest_match_only=False on Inverse is in effect.
matcher = Inverse(matcher0, longest_match_only=False)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"This is",
"This",
"is",
}
# longest_match_only=True on Inverse is in effect.
matcher = Inverse(matcher0, longest_match_only=True)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {"This is"}
# Check if Inverse raises an error when no child matcher is provided.
with pytest.raises(ValueError):
Inverse()
# Check if Inverse raises an error when two child matchers are provided.
with pytest.raises(ValueError):
Inverse(matcher0, matcher0)
def test_dictionary_match(doc_setup):
"""Test DictionaryMatch matcher."""
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=1)
# Test with a list of str
matcher = DictionaryMatch(d=["this"])
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {"This"}
# Test without a dictionary
with pytest.raises(Exception):
DictionaryMatch()
# TODO: test with plural words
matcher = DictionaryMatch(d=["is"], stemmer=PorterStemmer())
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {"is"}
# Test if matcher raises an error when _f is given non-TemporarySpanMention
matcher = DictionaryMatch(d=["this"])
with pytest.raises(ValueError):
list(matcher.apply(doc.sentences[0].words))
def test_lambda_function_matcher(doc_setup):
"""Test DictionaryMatch matcher."""
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=1)
# Test with a lambda function
matcher = LambdaFunctionMatcher(func=lambda x: True)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"This",
"is",
"apple",
}
# Test if matcher raises an error when _f is given non-TemporarySpanMention
with pytest.raises(ValueError):
list(matcher.apply(doc.sentences[0].words))
# Test if an error raised when a func is not provided.
with pytest.raises(Exception):
LambdaFunctionMatcher()
def test_get_ngrams_that_match_in_string(doc_setup):
"""Test if ngrams can be obtained even if they match mention's span in string."""
doc = doc_setup
sentence: Sentence = doc.sentences[0]
# Assert this sentence is visual.
assert sentence.is_visual()
# Assert this sentence is not tabular.
assert not sentence.is_tabular()
# Create 1-gram span mentions
space = MentionNgrams(n_min=1, n_max=1)
mentions = [tc for tc in space.apply(doc)]
assert len(mentions) == len(
[word for sent in doc.sentences for word in sent.words])
# Pick "is" from the apple sentence that matches "is" in the orange sentence.
mention = mentions[1]
assert mention.get_span() == "is"
# Check if the "is" in the orange sentence can be obtained.
ngrams = list(get_horz_ngrams(mention, from_sentence=False))
assert "is" in ngrams
def test_cancat(doc_setup):
"""Test Concat matcher."""
doc = doc_setup
space = MentionNgrams(n_min=1, n_max=2)
# Match any span that contains "this"
matcher0 = RegexMatchSpan(rgx=r"this",
search=False,
full_match=False,
longest_match_only=False)
# Match any span that contains "is"
matcher1 = RegexMatchSpan(rgx=r"is",
search=False,
full_match=False,
longest_match_only=False)
matcher = Concat(matcher0, matcher1)
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"This is"}
# Test if matcher raises an error when _f is given non-TemporarySpanMention
with pytest.raises(ValueError):
list(matcher.apply(doc.sentences[0].words))
# Test if an error is raised when the number of child matchers is not 2.
matcher = Concat(matcher0)
with pytest.raises(ValueError):
list(matcher.apply(space.apply(doc)))
# Test with left_required=False
matcher = Concat(matcher0, matcher1, left_required=False)
assert set(tc.get_span() for tc in matcher.apply(space.apply(doc))) == {
"This is",
"is apple",
}
# Test with right_required=False
matcher = Concat(matcher0, matcher1, right_required=False)
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"This is"}
# Test with permutations=False
matcher = Concat(matcher1, matcher0, permutations=False)
assert set(matcher.apply(space.apply(doc))) == set()
# Test with permutations=True
matcher = Concat(matcher1, matcher0, permutations=True)
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"This is"}
def test_get_vert_ngrams(doc_setup):
"""Test if get_vert_ngrams works."""
doc = doc_setup
sentence: Sentence = doc.sentences[0]
# Assert this sentence is visual.
assert sentence.is_visual()
# Assert this sentence is not tabular.
assert not sentence.is_tabular()
# Create 1-gram span mentions
space = MentionNgrams(n_min=1, n_max=1)
mentions = [tc for tc in space.apply(doc)]
assert len(mentions) == len(
[word for sent in doc.sentences for word in sent.words])
# Pick "apple" span mention.
mention = mentions[2]
assert mention.get_span() == "apple"
# from_sentence=True (ie ngrams from all aligned Sentences but its Sentence)
ngrams = list(get_vert_ngrams(mention))
assert ngrams == ["where", "is", "banaba", "?"]
def test_get_horz_ngrams(doc_setup):
"""Test if get_horz_ngrams works."""
doc = doc_setup
sentence: Sentence = doc.sentences[0]
# Assert this sentence is visual.
assert sentence.is_visual()
# Assert this sentence is not tabular.
assert not sentence.is_tabular()
# Create 1-gram span mentions
space = MentionNgrams(n_min=1, n_max=1)
mentions = [tc for tc in space.apply(doc)]
assert len(mentions) == len(
[word for sent in doc.sentences for word in sent.words])
# Pick "apple" span mention.
mention = mentions[2]
assert mention.get_span() == "apple"
# from_sentence=True (ie ngrams from all aligned Sentences but its Sentence)
ngrams = list(get_horz_ngrams(mention))
assert ngrams == ["that", "is", "orange", "."]
# Check the from_sentence=False (ie all aligned ngrams but itself)
assert mention.get_span() == "apple"
ngrams = list(get_horz_ngrams(mention, from_sentence=False))
assert ngrams == ["this", "is", ".", "that", "is", "orange", "."]
# Check attrib="lemmas"
ngrams = list(get_horz_ngrams(mention, attrib="lemmas"))
assert ngrams == ["that", "be", "orange", "."]
# Check attrib="pos_tags"
ngrams = list(get_horz_ngrams(mention, attrib="pos_tags"))
assert ngrams == ["dt", "vbz", "jj", "."]
# Check lower option
ngrams = list(get_horz_ngrams(mention, lower=False, from_sentence=False))
assert ngrams == ["This", "is", ".", "That", "is", "orange", "."]
# Pick "This" span mention.
mention = mentions[0]
assert mention.get_span() == "This"
ngrams = list(get_horz_ngrams(mention, from_sentence=False))
assert ngrams == ["is", "apple", ".", "that", "is", "orange", "."]
# Check n_max=2
ngrams = list(get_horz_ngrams(mention, n_max=2, from_sentence=False))
assert ngrams == [
"is apple",
"apple.",
"is",
"apple",
".",
"that is",
"is orange",
"orange.",
"that",
"is",
"orange",
".",
]
def test_ner_matchers():
"""Test different ner type matchers."""
# Set up a document
doc = Document(id=1, name="test", stable_id="1::document:0:0")
doc.text = " ".join([
"Tim Cook was born in USA in 1960.",
"He is the CEO of Apple.",
"He sold 100 million of iPhone.",
])
lingual_parser = SpacyParser("en")
for parts in lingual_parser.split_sentences(doc.text):
parts["document"] = doc
Sentence(**parts)
# Manually attach ner_tags as the result from spacy may fluctuate.
doc.sentences[0].ner_tags = [
"PERSON",
"PERSON",
"O",
"O",
"O",
"GPE",
"O",
"DATE",
"O",
]
doc.sentences[1].ner_tags = ["O", "O", "O", "O", "O", "ORG", "O"]
doc.sentences[2].ner_tags = [
"O", "O", "CARDINAL", "CARDINAL", "O", "MISC", "O"
]
# the length of words and that of ner_tags should match.
assert len(doc.sentences[0].words) == len(doc.sentences[0].ner_tags)
assert len(doc.sentences[1].words) == len(doc.sentences[1].ner_tags)
space = MentionNgrams(n_min=1, n_max=2)
# Test if PersonMatcher works as expected
matcher = PersonMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"Tim Cook"}
# Test if LocationMatcher works as expected
matcher = LocationMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"USA"}
# Test if DateMatcher works as expected
matcher = DateMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"1960"}
# Test if OrganizationMatcher works as expected
matcher = OrganizationMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"Apple"}
# Test if NumberMatcher works as expected
matcher = NumberMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"100 million"}
# Test if MiscMatcher works as expected
matcher = MiscMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"iPhone"}